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  • 101.
    Niu, Xin
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Multitemporal RADARSAT-2 polarimetric SAR data for urban cover classification using support vector machine2010In: 30th EARSeL Symposium, Paris, France, June, 2010, 2010, p. 581-588Conference paper (Refereed)
    Abstract [en]

    This research investigates the various RADARSAT-2 polarimetric SAR features for urban land cover classification using object-based method combining with support vector machine (SVM) and ruled-based approach. Six-dates of RADARSAT-2 fine-beam polarimetric SAR data were acquired in the rural-urban fringe of Greater Toronto Area during June to September, 2008. The major landuse/land-cover classes were high-density built-up areas, low-density built-up areas, roads, forests, parks, golf courses, water and several types of agricultural crops. The polarimetric SAR features examined are the parameters from Pauli, Freeman and Cloude-Pottier decompositions as well as the elements from coherence matrix and the intensities and their logarithm form of each channel. For urban land cover classification, SVM is combined with rule-based method for the object-based classification. The image objects containing the multitemporal polarimetric features were classified using the SVM classifier first. The SVM classification results were further refined using a rule-based approach. Rules were built to recognize specific classes defined by the shape features and the spatial relationships within the context. In terms of the effectiveness of different SAR ploarimtric parameters, the results indicated that the processed Pauli feature set could produce best classification result while the use of all the polarimetric features did not produce the best classification result. The raw Pauli parameters could generate similar result as all T elements. The logarithm parameters such as log intensity and processed Pauli parameters perform better than the intensity and raw Pauli respectively. The proposed object-based classification using SVM and rule-based approach yielded higher classification accuracies than the object-based classification using nearest neighbor classifier. 

     

     

     

  • 102.
    Niu, Xin
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    RADARSAT-2 fine-beam polarimetric and ultra-fine beam SAR data for urban land cover mapping: Comparison and SynergyManuscript (preprint) (Other academic)
    Abstract [en]

    This paper investigates the capabilities of multitemporal RADARSAT-2 fine-beam polarimetric SAR (PolSAR) data and ultra-fine beam C-HH SAR data for the detailed urban land cover mapping using a novel contextual approach. With an adaptive Markov Random Field (MRF) and the spatially variant Finite Mixture Model (FMM), contextual information was effectively explored to improve the mapping accuracy. The results showed that the contextual approach could produce homogenous classification while preserve shape details. Compared with C-HH SAR, PolSAR data were important for identify various urban patterns. Nevertheless, efficiency of the C-HH SAR textures for extraction of the built-up area was observed. Thus we proposed a texture enhancement in FMM to further improve the classification accuracy. Moreover, a rule-based approach employing object features and spatial relationships has been used to extract the road, street and park with reasonable accuracy. Three-date RADARSAT-2 fine-beam PolSAR and three-date ultra-fine beam C-HH SAR data over the Greater Toronto Area were used for the evaluation.  

  • 103.
    Niu, Xin
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    RADARSAT-2 polarimetric SAR data for urban land cover mapping using spatial- temporal SEM algorithm and mixture models2011In: 6th Joint Urban Remote Sensing Event (JURSE 2011), Munich, Germany, April 2011, 2011, p. 241-244Conference paper (Refereed)
  • 104.
    Niu, Xin
    et al.
    Natl Univ Def Technol, Peoples R China.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Dou, Yong
    RADARSAT-2 fine-beam polarimetric and ultra-fine-beam SAR data for urban mapping: comparison and synergy2016In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 37, no 12, p. 2810-2830Article in journal (Refereed)
    Abstract [en]

    The aim of this article is to investigate the capabilities of multitemporal RADARSAT-2 fine-beam polarimetric synthetic aperture radar (SAR) data and RADARSAT-2 ultra-fine-beam C-band single-polarization HH SAR (C-HH SAR) data for detailed urban land-cover mapping using a contextual approach. With an adaptive Markov random field and a spatially variant finite mixture model, contextual information was effectively explored to improve the mapping accuracy. A texture enhancement in FMM was further proposed to improve the classification accuracy. Moreover, a rule-based approach exploring object features and spatial relationships was employed to extract road, street, and park. Three-date RADARSAT-2 fine-beam polarimetric SAR (PolSAR) and three-date RADARSAT-2 ultra-fine-beam C-HH SAR data over the Greater Toronto area were used for the evaluation. For 10 major classes, the overall accuracy (OA) is 51% for C-HH SAR data and 79% for PolSAR data. Compared with C-HH SAR, PolSAR data produced better results for identifying various urban patterns. Although with multi-date, the C-HH SAR data showed low capability to distinguish high-density residential area and industry commercial area (Ind.). Considerable low-density residential area (LD) was misclassified as forest. Identification of the construction site (Cons.) and golf course were poor. Nevertheless, the efficiency of the multitemporal C-HH SAR textures for distinguishing the built-up areas was observed. By texture enhancement with the synergy of the PolSAR and C-HH SAR data, the mapping results could be significantly improved, especially for LD, forest, and crops. The OA is improved by 2.7% for PolSAR data, and 11.1% for C-HH SAR data. Road, street, and park could be extracted by the rule-based approach with OA about 77% for 13 classes.

  • 105.
    Otosaka, Inès
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    A Historic Record of Sea Ice Extents from Scatterometer Data2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Sea ice is a vital component of the cryosphere and does not only influence the polar regions but has a more global influence. Indeed, sea ice plays a major role in the regulation of the global climate system as the sea ice cover reflects the sun radiation back to the atmosphere keeping the polar regions cool. The shrinkage of the sea ice cover entails the warming up of the oceans and as a consequence, a further amplification of the melting of sea ice. Therefore, the polar regions are sensitive to climate change and monitoring the sea ice cover is very important.

    To assess sea ice change in the polar regions, satellite active microwave sensors, scatterometers, are used to observe the evolution of sea ice extent and sea ice types. Thus, this research aims at creating a historic record of daily global Arctic and Antarctic sea ice extents and analysing the change in sea ice types with scatterometer data.

    A Bayesian sea ice detection algorithm, developed for the Advanced scatterometer (ASCAT), is applied and tuned to the configurations of the scatterometers on board the European Remote Sensing satellites, ERS\textendash 1 and ERS\textendash 2. The sea ice geophysical model functions (GMFs) of ERS and ASCAT are studied together to validate the use of ASCAT sea ice GMF extrapolated to the lower incidence angles of ERS. The main adaptations from the initial algorithm aim at compensating for the lower observation densities afforded by ERS with a refined spatial filter and time\textendash variable detection thresholds. To further analyse the backscatter response from sea ice and derive information on the different sea ice types, a new model of sea ice backscattering at C\textendash band is proposed in this study. This model has been derived using ERS and ASCAT backscatter data and describes the variation of sea ice backscatter with incidence angle as a function of sea ice type.

    The improvement of the sea ice detection algorithm for ERS\textendash 1 and ERS\textendash 2, operating between 1992 and 2001, leads to the extension of the existing records of daily global sea ice extents from the Quick scatterometer (QuikSCAT) which operated from 1999 to 2009 and ASCAT operating from 2007 onwards. The sea ice extents from ERS, QuikSCAT and ASCAT show excellent agreement during the overlapping periods, attesting to the consistency and homogeneity of the long\textendash term scatterometer sea ice record. The new climate record is compared against passive microwave derived sea ice extents, revealing consistent differences between spring and summer which are attributed to the lower sensitivity of the passive microwave technique to melting sea ice. The climate record shows that the minimum Arctic summer sea ice extent has been declining, reaching the lowest record of sea ice extent in 2012.

    The new model for sea ice backscatter is used on ERS and ASCAT backscatter data and provides a more precise normalization of sea ice backscatter than was previously available. An application of this model in sea ice change analysis is performed by classifying sea ice types based on their normalized backscatter values. This analysis reveals that the extent of multi\textendash year Arctic sea ice has been declining remarkably over the period covered by scatterometer observations.

  • 106.
    Ottonello-Briano, Floria
    et al.
    KTH, School of Electrical Engineering (EES), Micro and Nanosystems.
    Errando-Herranz, Carlos
    KTH, School of Electrical Engineering (EES), Micro and Nanosystems.
    Rödjegård, Henrik
    Senseair AB.
    Martin, Hans
    Senseair AB.
    Sohlström, Hans
    KTH, School of Electrical Engineering (EES), Micro and Nanosystems.
    Gylfason, Kristinn B.
    KTH, School of Electrical Engineering and Computer Science (EECS), Micro and Nanosystems.
    Carbon Dioxide Sensing with Low-confinementHigh-sensitivity Mid-IR SiliconWaveguides2019In: Conference on Lasers and Electro-Optics 2019: CLEO: Science and Innovations, 2019, article id STh1F.3Conference paper (Refereed)
    Abstract [en]

    We present a low-confinement Si waveguide for 4.26 μm wavelength and applyit to sense CO2 concentrations down to 0.1 %. We demonstrate the highest reportedwaveguide sensitivity to CO2: 44% of the free-space sensitivity.

  • 107. Piwowar, Joseph M.
    et al.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Foreword to the Special Issue on the Analysis of Multitemporal Remote Sensing Images2014In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 7, no 8, p. 3187-3189Article in journal (Refereed)
  • 108.
    Qin, Yuchu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Niu, Z.
    Chen, F.
    Li, B.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Object-based land cover change detection for cross-sensor images2013In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 34, no 19, p. 6723-6737Article in journal (Refereed)
    Abstract [en]

    Accurate and timely land cover change detection at regional and global scales is necessary for both natural resource management and global environmental change studies. Satellite remote sensing has been widely used in land cover change detection over the past three decades. The variety of satellites which have been launched for Earth Observation (EO) and the large volume of remotely sensed data archives acquired by different sensors provide a unique opportunity for land cover change detection. This article introduces an object-based land cover change detection approach for cross-sensor images. First, two images acquired by different sensors were stacked together and principal component analysis (PCA) was applied to the stacked data. Second, based on the Eigen values of the PCA transformation, six principal bands were selected for further image segmentation. Finally, a land cover change detection classification scheme was designed based on the land cover change patterns in the study area. An image-object classification was implemented to generate a land cover change map. The experiment was carried out using images acquired by Landsat 5 TM and IRS-P6 LISS3 over Daqing, China. The overall accuracy and kappa coefficient of the change map were 83.42% and 0.82, respectively. The results indicate that this is a promising approach to produce land cover change maps using cross-sensor images.

  • 109.
    Qin, Yuchu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Vu, Tuong Thuy
    University of Nottingham, Malaysia Campus.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Toward an Optimal Algorithm for LiDAR Waveform Decomposition2012In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 9, no 3, p. 482-486Article in journal (Refereed)
    Abstract [en]

    This letter introduces a new approach for light detection and ranging (LiDAR) waveform decomposition. First, inflection points are identified by the Ramer-Douglas-Peucker curve-fitting algorithm, and each inflection point has a corresponding baseline during curve fitting. Second, according to the spatial relation between the baseline and the inflection point, peaks are selected from the inflection points. The distance between each peak and its baseline and the maximum number of peaks are employed as a criterion to select a "significant" peak. Initial parameters such as width and boundaries of peaks provide restraints for the decomposition; right and left boundaries are estimated via a conditional search. Each peak is fitted by a Gaussian function separately, and other parts of the waveform are fitted as line segments. Experiments are implemented on waveforms acquired by both small-footprint LiDAR system LMS-Q560 and large-footprint LiDAR system Laser Vegetation Imaging Sensor. The results indicate that the algorithm could provide an optimal solution for LiDAR waveform decomposition.

  • 110.
    Qin, Yuchu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Yao, W.
    TU Munich.
    Vu, T.
    University of Nottingham, Malaysia.
    Li, S.
    China.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Characterizing radiometric attributes of point cloud using a normalized reflective factor derived from small footprint LiDAR waveform2015In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 8, no 2, p. 740-749, article id 6918370Article in journal (Refereed)
    Abstract [en]

    This paper presents a reflectance-like coefficient, normalized reflective factor (NRF) to characterize the radiometric attributes of point cloud generated from small footprint light detection and ranging (LiDAR) waveform data. The NRF is defined as a normalized ratio between the energy of emitted laser beam and the peak in return waveform in conjunction with the atmospheric attenuation and observation geometry. Based on the Gaussian parameters of the emitted and return waveforms, NRF is calculated with an empirical atmospheric model and user-defined standard observation geometry. To correct the radiometric measurement of point cloud in multipeak waveform, a semi-physical-based method is adopted to enhance the NRF of point cloud generated from multipeak waveform. Experiments are conducted with small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. A curve-fitting-based approach is applied to decompose LiDAR waveform into three-dimensional (3-D) coordinates of point cloud, and the NRF are calculated using the Gaussian parameters of both emitted and return waveforms. The visualization of the radiometric attributes of point cloud data is carried out over the overlapping areas between different flight strips, it suggests that the NRF over overlapping area is much smooth than the normalized intensity. Quantitative comparison with Hyperion data indicates that the NRF has much higher correlation with surface reflectance than the normalized intensity data. Standard deviations of NRF and the normalized intensity of different land cover patches are analyzed to assess the homogeneity of the radiometric data. It is observed that NRF has less variability than the normalized intensity within the same land cover patches. Point cloud of two sample trees is also selected to assess the performance of the “sub-footprint” effect correction. It is observed that the proposed approach reduced the variability of radiometric attributes over tree canopies with increa- ing NRF values; which means the “sub-footprint” effect is mitigated. In summary, the proposed NRF can serve as a promising indicator to characterize radiometric attribute of LiDAR point cloud.

  • 111. Ravanelli, R.
    et al.
    Nascetti, Andrea
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. University of Rome La Sapienza, Rome, Italy.
    Cirigliano, R. V.
    Di Rico, C.
    Monti, P.
    Crespi, M.
    Monitoring urban heat island through google earth engine: Potentialities and difficulties in different cities of the United States2018In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society for Photogrammetry and Remote Sensing , 2018, Vol. 42, no 3, p. 1467-1472Conference paper (Refereed)
    Abstract [en]

    The aim of this work is to exploit the large-scale analysis capabilities of the innovative Google Earth Engine platform in order to investigate the temporal variations of the Urban Heat Island phenomenon as a whole. A intuitive methodology implementing a large-scale correlation analysis between the Land Surface Temperature and Land Cover alterations was thus developed. The results obtained for the Phoenix MA are promising and show how the urbanization heavily affects the magnitude of the UHI effects with significant increases in LST. The proposed methodology is therefore able to efficiently monitor the UHI phenomenon.

  • 112. Rosina, K.
    et al.
    Hurbánek, P.
    Cebecauer, Matej
    KTH, School of Architecture and the Built Environment (ABE), Transport Science. Department of Transportation Networks, Faculty of Management Science and Informatics, University of Žilina.
    Using OpenStreetMap to improve population grids in Europe2016In: Cartography and Geographic Information Science, ISSN 1523-0406, E-ISSN 1545-0465, p. 1-13Article in journal (Refereed)
    Abstract [en]

    OpenStreetMap (OSM) database has previously been used to support spatial disaggregation of population data by partly masking out non-residential impervious areas in the European Copernicus imperviousness layer (IL). However, the exact procedure of OSM data incorporation is unknown, and its contribution to the improvement of estimation accuracy has never been studied. In this article, we present a sensitivity study to find out which road categories should be used for masking of IL and how the linear features might be transformed to raster representation. Using Austria and Slovenia as a study area, 2006 commune population counts are disaggregated into 100 m grid cells using 12 versions of modified IL. Further tuning of estimates is performed using CORINE Land Cover (CLC) data in an iterative algorithm. Disaggregated grids are then validated against reference 1 km census-based data. The results show that overall error was reduced thanks to OSM incorporation in all tested scenarios, although the relative improvement varies between as well as within the two countries. The best result (5.3% reduction) was achieved using railways and three major road categories (motorway, trunk, and primary) with double exaggeration of width.

  • 113.
    Rymasheuskaya, Maryna
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301). Polotsk State University, Novopolotsk, Belarus .
    Comparison of several change detection methods for monitoring land cover dynamics in Belarus2007In: Proceedings of MultiTemp 2007 - 2007 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2007, p. 4293063-Conference paper (Refereed)
    Abstract [en]

    The study presents experience of use of post-classification comparison, image differencing, principal component analysis, use of ancillary data and combinations of different change detection methods for detecting land cover / land use dynamics in Novopolotsk area, Belarus over the period 1994-2002. All methods give good results in terms of accuracy assessment, visual presentation, etc. Change detection results are dependent on parameters applied.

  • 114. Sarker, M. L. R.
    et al.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Nichol, J.
    Comparison of pixel-based, object-based and sequential masking classification procedures for land use and land cover mapping using multiple sensor SAR in SWEDEN2007In: 28th Asian Conference on Remote Sensing 2007, ACRS 2007, 2007, p. 623-628Conference paper (Refereed)
    Abstract [en]

    Multiple sensor applications have become increasingly common in recent years and offer new opportunities to the remote sensing community to extract better information about the earth surface. However, the processing of multiple sensor SAR for land use and land cover mapping is not straightforward and still needs more investigation in order to become operational. This study investigates the efficiency of three different types of classification procedures, namely pixel-based, object-based and sequential masking to extract land use and land cover information from multiple sensor SAR images using the same training and validation areas. Four sensors (RADARSAT finebeam, RADARSAT standard-beam, ERS-2, and JERS-1) in different combinations were investigated in two study areas, to compare their effectiveness for accurate land cover mapping. The results indicate that the pixel-based classifier namely ANN is more accurate (around 90% overall accuracy and 0.90 Kappa coefficient) compared with object-based classification for extracting land use and land cover information from multiple sensor SAR. Overall it was found that the best performance (more than 90% overall accuracy and more than. 90 Kappa coefficient) can be achieved using a sequential masking approach because of its step by step classification technique.

  • 115.
    Sarker, M.L.R.
    et al.
    University of Hong Kong.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Nichol, J.
    University of Hong Kong.
    Comparison of Pixel-based, Object-Based and Sequential Masking Classification Procedures for Land Use and Land Cover Mapping using Multiple Sensors SAR in Sweden2008In: Asian Journal of Geoinformatics, ISSN 1513-6728, Vol. 8, no 1, p. 25-30Article in journal (Refereed)
  • 116. Shao, Quanqin
    et al.
    Sun, C.
    Liu, J.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Kuang, W.
    Impact of Urban Expansion on Meteorological Observation Data and Over-estimation to Regional Air Temperature in China2009In: ACTA GEOGRAPHICA SINICA/地理学报, ISSN 0375-5444, Vol. 64, no 11, p. 1292-1302Article in journal (Refereed)
  • 117.
    Sjöberg, Lars E.
    KTH.
    On the topographic bias and density distribution in modelling the geoid and orthometric heights2018In: Journal of Geodetic Science, ISSN 2081-9919, E-ISSN 2081-9943, Vol. 8, no 1, p. 30-33Article in journal (Refereed)
    Abstract [en]

    It is well known that the success in precise determinations of the gravimetric geoid height (N) and the orthometric height (H) rely on the knowledge of the topographic mass distribution. We show that the residual topographic bias due to an imprecise information on the topographic density is practically the same for N and H, but with opposite signs. This result is demonstrated both for the Helmert orthometric height and for a more precise orthometric height derived by analytical continuation of the external geopotential to the geoid. This result leads to the conclusion that precise gravimetric geoid heights cannot be validated by GNSS-levelling geoid heights in mountainous regions for the errors caused by the incorrect modelling of the topographic mass distribution, because this uncertainty is hidden in the difference between the two geoid estimators.

  • 118.
    Sjöberg, Lars E.
    et al.
    KTH.
    Joud, M. S. S.
    Div Geodesy & Satellite Positioning, Stockholm, Sweden..
    A numerical test of the topographic bias2018In: Journal of Geodetic Science, ISSN 2081-9919, E-ISSN 2081-9943, Vol. 8, no 1, p. 14-17Article in journal (Refereed)
    Abstract [en]

    In 1962 A. Bjerhammar introduced the method of analytical continuation in physical geodesy, implying that surface gravity anomalies are downward continued into the topographic masses down to an internal sphere (the Bjerhammar sphere). The method also includes analytical upward continuation of the potential to the surface of the Earth to obtain the quasigeoid. One can show that also the common remove-compute-restore technique for geoid determination includes an analytical continuation as long as the complete density distribution of the topography is not known. The analytical continuation implies that the downward continued gravity anomaly and/or potential are/is in error by the so-called topographic bias, which was postulated by a simple formula of L E Sjoberg in 2007. Here we will numerically test the postulated formula by comparing it with the bias obtained by analytical downward continuation of the external potential of a homogeneous ellipsoid to an inner sphere. The result shows that the postulated formula holds: At the equator of the ellipsoid, where the external potential is downward continued 21 km, the computed and postulated topographic biases agree to less than a millimetre (when the potential is scaled to the unit of metre).

  • 119.
    Stromann, Oliver
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Feature Extraction and FeatureSelection for Object-based LandCover Classification: Optimisation of Support Vector Machines in aCloud Computing Environment2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Mapping the Earth’s surface and its rapid changes with remotely sensed data is a crucial tool to un-derstand the impact of an increasingly urban world population on the environment. However, the impressive amount of freely available Copernicus data is only marginally exploited in common clas-sifications. One of the reasons is that measuring the properties of training samples, the so-called ‘fea-tures’, is costly and tedious. Furthermore, handling large feature sets is not easy in most image clas-sification software. This often leads to the manual choice of few, allegedly promising features. In this Master’s thesis degree project, I use the computational power of Google Earth Engine and Google Cloud Platform to generate an oversized feature set in which I explore feature importance and analyse the influence of dimensionality reduction methods. I use Support Vector Machines (SVMs) for object-based classification of satellite images - a commonly used method. A large feature set is evaluated to find the most relevant features to discriminate the classes and thereby contribute most to high clas-sification accuracy. In doing so, one can bypass the sensitive knowledge-based but sometimes arbi-trary selection of input features.Two kinds of dimensionality reduction methods are investigated. The feature extraction methods, Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA), which transform the original feature space into a projected space of lower dimensionality. And the filter-based feature selection methods, chi-squared test, mutual information and Fisher-criterion, which rank and filter the features according to a chosen statistic. I compare these methods against the default SVM in terms of classification accuracy and computational performance. The classification accuracy is measured in overall accuracy, prediction stability, inter-rater agreement and the sensitivity to training set sizes. The computational performance is measured in the decrease in training and prediction times and the compression factor of the input data. I conclude on the best performing classifier with the most effec-tive feature set based on this analysis.In a case study of mapping urban land cover in Stockholm, Sweden, based on multitemporal stacks of Sentinel-1 and Sentinel-2 imagery, I demonstrate the integration of Google Earth Engine and Google Cloud Platform for an optimised supervised land cover classification. I use dimensionality reduction methods provided in the open source scikit-learn library and show how they can improve classification accuracy and reduce the data load. At the same time, this project gives an indication of how the exploitation of big earth observation data can be approached in a cloud computing environ-ment.The preliminary results highlighted the effectiveness and necessity of dimensionality reduction methods but also strengthened the need for inter-comparable object-based land cover classification benchmarks to fully assess the quality of the derived products. To facilitate this need and encourage further research, I plan to publish the datasets (i.e. imagery, training and test data) and provide access to the developed Google Earth Engine and Python scripts as Free and Open Source Software (FOSS).

  • 120.
    Sun, Jing
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Vu, T. T.
    Distributed and hierarchical object-based image analysis for damage assessment: a case study of 2008 Wenchuan earthquake, China2016In: Geomatics, Natural Hazards and Risk, ISSN 1947-5705, E-ISSN 1947-5713, p. 1-11Article in journal (Refereed)
    Abstract [en]

    Object-based image analysis (OBIA) is an emerging technique for analyzing remote sensing image based on object properties including spectral, geometry, contextual and texture information. To reduce the computational cost of this comprehensive OBIA and make it more feasible in disaster responses, we developed a unique approach – distributed and hierarchical OBIA approach for damage assessment. This study demonstrated a completed classification of YingXiu town, heavily devastated by the 2008 Wenchuan earthquake using Quickbrid imagery. Two distinctive areas, mountainous areas and urban, were analyzed separately. This approach does not require substantial processing power and large amounts of available memory because image of a large disaster-affected area was split in smaller pieces. Two or more computers could be used in parallel to process and analyze these sub-images based on different requirements. The approach can be applicable in other cases whereas the established set of rules can be adopted in similar study areas. More experiments will be carried out in future studies to prove its feasibility.

  • 121. Tang, A.
    et al.
    Jacobsson, Krister
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Andrew, L. L. H.
    Low, S. H.
    An accurate link model and its application to stability analysis of FAST TCP2007In: INFOCOM 2007, IEEE , 2007, p. 161-169Conference paper (Refereed)
    Abstract [en]

    This paper presents a link model which captures the queue dynamics when congestion windows of TCP sources change. By considering both the self-clocking and the link integrator effects, the model is a generalization of existing models and is shown to be more accurate by both open loop and closed loop packet level simulations. It reduces to the known static link model when flows' round trip delays are similar, and approximates the standard integrator link model when the heterogeneity of round trip delays is significant. We then apply this model to the stability analysis of FAST TCP. It is shown that FAST TCP flows over a single link are always linearly stable regardless of delay distribution. This result resolves the notable discrepancy between empirical observations and previous theoretical predictions. The analysis highlights the critical role of self-clocking in TCP stability and the scalability of FAST TCP with respect to delay. The proof technique is new and less conservative than the existing ones.

  • 122.
    Vu, Tuong Thuy
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Fusion of ENVISAT ASAR and HJ-1 Multispectral Data for Urban Land Cover Mapping2010In: Proceedings, ESA Living Planet Symposium, 2010, 2010Conference paper (Other academic)
  • 123.
    Wang, Wei
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Xiang, Deliang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Zhang, Jun
    Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha, Hunan, Peoples R China..
    Wan, Jianwei
    Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha, Hunan, Peoples R China..
    Enhanced edge detection for polarimetric SAR images using a directional span-driven adaptive window2018In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 39, no 19, p. 6340-6357Article in journal (Refereed)
    Abstract [en]

    Automatic edge detection for polarimetric synthetic aperture radar (PolSAR) images plays a fundamental role in various PolSAR applications. The classic methods apply the fixed-shape windows to detect the edges, whereas their performance is limited in heterogeneous areas. This article presents an enhanced edge detection method for PolSAR data based on the directional span-driven adaptive (DSDA) window. The DSDA window has variable sizes and flexible shapes, and is constructed by adaptively selecting samples that follow the same statistical distribution. Therefore, it can overcome the limitation of classic fixed-shape windows. To obtain refined and reliable edge detection results in heterogeneous urban areas, we adopt the spherically invariant random vector (SIRV) product model since the complex Wishart distribution is often not met. In addition, a span ratio is combined with the SIRV distance to highlight the dissimilarity measure and to improve the robustness of the proposed method. The simulated PolSAR data and three real data sets from experimental synthetic aperture radar, electromagnetics institute synthetic aperture radar, and Radarsat-2 systems are used to validate the performance of the enhanced edge detector. Both quantitative evaluation and visual presentation of the results demonstrate the effectiveness of the proposed method and its superiority over the classic edge detectors.

  • 124. Weng, Qihao
    et al.
    Gamba, Paolo
    Mountrakis, G.
    Pesaresi, M.
    Lu, L.
    Kemper, T.
    Heinzel, J.
    Xian, G.
    Jin, H
    Miyazaki, H.
    Xu, B.
    Quresh, S.
    Keramitsoglou, I.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Esch, T.
    Roth, A,
    Elvidge, C. D.
    Chapter 4. Urban Observing Sensors2014In: Global Urban Monitoring and Assessment through Earth Observation / [ed] Qihao Weng, CRC Press, 2014, p. 49-80Chapter in book (Refereed)
  • 125. Xia, J.
    et al.
    Du, P.
    Liu, P.
    Shan, D.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Quantifying spatio-temporal change of urban land cover/use and landscape pattern using BJ-1 and CBERS remote sensing images: A case study of Shanghai2010In: 2010 Dragon 2 Mid Term Results Symposium, 2010Conference paper (Other academic)
    Abstract [en]

    The overall objective of this research is to investigate the effectiveness of BJ-1 and CBERS remote sensing images for monitoring and modeling the spatial-temporal pattern change in Shanghai city. In this study, a scene of BJ-1 and two scenes of CBERS 01/02 images were used as the data sources. Comparing the accuracy of maximum likelihood classifier (MLC) and support vector machine (SVM) with two different kernel functions, the highest one in each temporal was chosen to analyze the typical ground objects and landscape pattern change. Gradient and direction characteristic analysis were also applied to calculate the degree and direction of urban growth. We selected some human and natural indicators from the Shanghai Statistical Yearbook to analyze the driving force of Shanghai. The results indicated that urbanization in Shanghai tended to be marginalization. And the urban growth has occurred in the NE-E and SE-S direction regions (Pudong New Area and Sanlin Area).

  • 126. Xia, J.
    et al.
    Du, P.
    Zhang, H.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Yuan, L.
    Monitoring urban impervious surface growth in Xuzhou using CBERS and BJ-1 Remote Sensing images2010In: 2010 Dragon 2 Mid Term Results Symposium, 2010Conference paper (Other academic)
    Abstract [en]

    As the study area, Xuzhou City was chosen, located in the northwestern of Jiangsu Province, China. And two CBERS images and one BJ-1 small satellite image were employed to impervious surface extraction. Using multi-layer perception (MLP) neural network, all pixels were decomposed to the four fraction images representing the abundance of four endmembers: vegetation, high-albedo objects, low-albedo objects and soil. Then, the impervious surface was derived by the combination of high-albedo and low-albedo fraction images after removing the influence of water body. Furthermore, ALOS Pan high resolution image, that covering the city center of study area were selected to validate the impervious surface estimation results and evaluate the accuracy of impervious surface extraction. By comparing the urban impervious surface abundance from three remote sensing images, the change pattern of impervious surface was studied. The past years saw the impervious surface had been grown rapidly in Xuzhou City, especially in the north-east region, and south-east (Tongshan New District, Nanhu Campus of China University of Mining and Technology (CUMT)).

  • 127. Xia, Junshi
    et al.
    Du, Peijun
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Evaluation of spatial pattern of urban heat island and its relationship with land cover by HJ-1 remote sensing images - A case study of Shanghai City2010In: 2010 Dragon 2 Mid Term Results Symposium, 2010Conference paper (Refereed)
    Abstract [en]

    In the study, multi-spectral data with 30m resolution and hyperspectral images with 100m resolution are used to classify the land cover into six types: water body, public green space, agriculture land, built-up areas and non-use land, clouds, while thermal images with 300m resolution are used to evaluate the urban heat island effect. The field work at Sep 17th, 2009 is used to evaluate the accuracy of classification results. In order to quantify the degree along the rural-urban gradient, Moran's I index and semi-variance are used to assess the spatial autocorrection and describe the scale and pattern of spatial variability. The results show that the land cover map resulted from multi-spectral image has satisfactory accuracy. From the results of Moran' I index and semi-variance, it indicats that spatial pattern of homogeneous patches exist on small scales smaller 36km, meso scales between 36-81km and large scales bigger than 81km. The relationship between land cover types and UHI patterns is also studied.

  • 128.
    Xiang, Deliang
    KTH, School of Architecture and the Built Environment (ABE).
    Edge Detector for Polarimetric SAR ImagesUsing SIRV Model and Gauss-Shaped FilterIn: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571Article in journal (Refereed)
    Abstract [en]

    The classic constant false alarm rate (CFAR) edgedetector with rectangle-shaped filter has been proven to beeffective and widely used in polarimetric SAR (PolSAR) images.However, in practical use, the assumption of complex Wishartdistribution is often not respected, especially in heterogeneousurban areas. In addition, as a simple smoothing filter, therectangle-shaped window is often shown to be easy to incur falseedge pixels near true edges. Therefore, its performance islimited. To overcome this restriction, we propose a new edgedetector for PolSAR images, which utilizes the sphericallyinvariant random vector (SIRV) product model to estimate thenormalized covariance matrix for each pixel and then replacethe rectangle-shaped filter with Gauss-shaped filter. Theperformance of our proposed methodology is presented andanalyzed on two real PolSAR data sets, and the results show thatthe new edge detector attains better performance than theclassic one, particularly for urban areas.

  • 129.
    Xiang, Deliang
    KTH, School of Architecture and the Built Environment (ABE). National University of Defense Technology, China.
    The cross-scattering component of polarimetric SAR in urban areasand its application to model-based scattering decompositionIn: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901Article in journal (Refereed)
    Abstract [en]

    After the work of Freeman, Durden, Pottier, and Yamaguchi, manydecomposition techniques have been proposed for urban areas, mainly to resolvethe overestimation problem of volume scattering. Since it has been validated thatthe cross-polarised (HV) scattering is caused not only by forests but also byrotated dihedrals, in this paper, we propose a cross scattering coherency matrix tomodel the HV component from orientated and complex buildings and thendemonstrate its performance on model-based scattering decomposition. Thebuilding orientation angle is considered in this coherency matrix, making itflexible and adaptive in the decomposition. Therefore, the HV components fromforests and orientated urban areas can be modelled respectively. Twodecomposition procedures are applied in this paper. The first one is to validatethe effectiveness of this scattering model. We regard the HV component fromurban areas as cross scattering, which is an independent scattering componentadded to the Yamaguchi four-component decomposition. Another one is theurban area decomposition application using this scattering model. Decompositionis implemented for urban and natural areas respectively and the HV componentfrom urban areas is regarded as their volume scattering. This procedure is similarto many other state-of-the-art methods for urban areas and needs to discriminatethe urban and natural areas before decomposition. Spaceborne Radarsat-2 C band,the Airborne Synthetic Aperture Radar (AIRSAR) L band and UninhabitedAerial Vehicle Synthetic Aperture Radar (UAVSAR) L band full polarimetricSAR data are used to validate the performance of this cross scattering coherencymatrix. The HV component of orientated buildings is generated, leading to abetter decomposition result for urban areas.

  • 130.
    Xiang, Deliang
    KTH, School of Architecture and the Built Environment (ABE).
    Urban Area Information Extraction From Polarimetric SAR Data2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Polarimetric Synthetic Aperture Radar (PolSAR) has been used for various remote sensing applications since more information could be obtained in multiple polarizations. The overall objective of this thesis is to investigate urban area information extraction from PolSAR data with the following specific objectives: (1) to exploit polarimetric scattering model-based decomposition methods for urban areas, (2) to investigate effective methods for man-made target detection, (3) to develop edge detection and superpixel generation methods, and (4) to investigate urban area classification and segmentation.

    Paper 1 proposes a new scattering coherency matrix to model the cross-polarized scattering component from urban areas, which adaptively considers the polarization orientation angles of buildings. Thus, the HV scattering components from forests and oriented urban areas can be modelled respectively. Paper 2 presents two urban area decompositions using this scattering model. After the decomposition, urban scattering components can be effectively extracted.

    Paper 3 presents an improved man-made target detection method for PolSAR data based on nonstationarity and asymmetry. Reflection asymmetry was incorporate into the azimuth nonstationarity extraction method to improve the man-made target detection accuracy, i.e., removing the natural areas and detecting the small targets.

    In Paper 4, the edge detection of PolSAR data was investigated using SIRV model and Gauss-shaped filter. This detector can locate the edge pixels accurately with fewer omissions. This could be useful for speckle noise reduction, superpixel generation and others.

    Paper 5 investigates an unsupervised classification method for PolSAR data in urban areas. The ortho and oriented buildings can be discriminated very well. Paper 6 proposes an adaptive superpixel generation method for PolSAR images. The algorithm produces compact superpixels that can well adhere to image boundaries in both natural and urban areas.

  • 131.
    Xiang, Deliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE). College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Satellite Positioning.
    Su, Yi
    College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China.
    Model-Based Decomposition With Cross Scattering for Polarimetric SAR Urban Areas2015In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 12, no 12, p. 2496-2500Article in journal (Refereed)
    Abstract [en]

    Cross-polarized scattering (HV) is not only caused by vegetation but also by rotated dihedrals. In this letter, we use rotated dihedral corner reflectors to form a cross scattering matrix and propose an extended model-based decomposition method for polarimetric synthetic aperture radar (PolSAR) data over urban areas. Unlike other urban decomposition techniques which need to discriminate between urban and natural areas before decomposition, this proposed method is applied directly on the PolSAR image. The building orientation angle is considered in this scattering matrix, making it flexible and adaptive in the decomposition process. This enables the separation of the cross scattering of urban areas from the overall HV component. The cross and helix scattering components are also compared in this study. RADARSAT-2 quad-pol C band and AIRSAR L band data are used to validate the performance of the proposed method. The cross scattering power of oriented buildings is generated, leading to a better decomposition result for urban areas with respect to other urban decomposition techniques.

  • 132.
    Xiang, Deliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China.
    Ban, Yifang
    Su, Yi
    The cross-scattering component of polarimetric SAR in urban areas and its application to model-based scattering decomposition2016In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 37, no 16Article in journal (Refereed)
    Abstract [en]

    After the work of Freeman, Durden, Pottier, and Yamaguchi, manydecomposition techniques have been proposed for urban areas, mainly to resolvethe overestimation problem of volume scattering. Since it has been validated thatthe cross-polarised (HV) scattering is caused not only by forests but also byrotated dihedrals, in this paper, we propose a cross scattering coherency matrix tomodel the HV component from orientated and complex buildings and thendemonstrate its performance on model-based scattering decomposition. Thebuilding orientation angle is considered in this coherency matrix, making itflexible and adaptive in the decomposition. Therefore, the HV components fromforests and orientated urban areas can be modelled respectively. Twodecomposition procedures are applied in this paper. The first one is to validatethe effectiveness of this scattering model. We regard the HV component fromurban areas as cross scattering, which is an independent scattering componentadded to the Yamaguchi four-component decomposition. Another one is theurban area decomposition application using this scattering model. Decompositionis implemented for urban and natural areas respectively and the HV componentfrom urban areas is regarded as their volume scattering. This procedure is similarto many other state-of-the-art methods for urban areas and needs to discriminatethe urban and natural areas before decomposition. Spaceborne Radarsat-2 C band,the Airborne Synthetic Aperture Radar (AIRSAR) L band and UninhabitedAerial Vehicle Synthetic Aperture Radar (UAVSAR) L band full polarimetricSAR data are used to validate the performance of this cross scattering coherencymatrix. The HV component of orientated buildings is generated, leading to abetter decomposition result for urban areas.

  • 133.
    Xiang, Deliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China.
    Wang, Wei
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China.
    Su, Yi
    Adaptive Superpixel Generation for Polarimetric SAR Images With Local Iterative Clustering and SIRV Model2017In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 55, no 6, p. 3115-3131Article in journal (Refereed)
    Abstract [en]

    Simple linear iterative clustering (SLIC) algorithm was proposed for superpixel generation on optical images and showed promising performance. Several studies have been proposed to modify SLIC to make it applicable for polarimetric synthetic aperture radar (PolSAR) images, where the Wishart distance is adopted as the similarity measure. However, the superpixel segmentation results of these methods were not satisfactory in heterogeneous urban areas. Further, it is difficult to determine the tradeoff factor which controls the relative weight between polarimetric similarity and spatial proximity. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel generation method is proposed to overcome these limitations. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel generation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed superpixel generation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.

  • 134.
    Xiang, Deliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China.
    Tang, Tao
    National University of Defense Technology, China.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Su, Yi
    National University of Defense Technology, China.
    Man-Made Target Detection from Polarimetric SAR Data via Nonstationarity and Asymmetry2016In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 9, no 4, p. 1459-1469, article id 7405260Article in journal (Refereed)
    Abstract [en]

    Detection of man-made targets in urban areas using polarimetric synthetic aperture radar (PolSAR) data has become a promising research area since it has a close relationship with urban planning, rescue service, etc. This paper presents an improved man-made target detection method for PolSAR data based on nonstationarity and asymmetry. Nonstationarity in azimuth direction is already utilized to separate man-made and natural targets in urban areas. However, there are still some drawbacks. Some small man-made targets and roads cannot be effectively detected. In addition, nonstationarity can also occur in some other natural surfaces, such as cropland with Bragg resonance. Therefore, to resolve these problems, we incorporate reflection asymmetry into the azimuth nonstationarity extraction method to improve the man-made target detection accuracy, i.e., removing the natural areas and detecting the small targets. Airborne ESAR data and spaceborne PALSAR data are used to validate the performance of the proposed method. The result obtained by our proposed method shows a 20% higher accuracy than the result based on original nonstationarity extraction method. Natural areas with Bragg resonance are removed. Moreover, most of the buildings and some metallic fences along the road can also be accurately detected.

  • 135.
    Yifang, Ban
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gong, Peng
    Tsinghua University.
    Gini, Chandra
    Global land cover mapping using earth observation satellite data: recent progresses and challenges2015In: ISPRS journal of photogrammetry and remote sensing (Print), ISSN 0924-2716, E-ISSN 1872-8235, Vol. 103, no 1, p. 1-6Article in journal (Refereed)
  • 136.
    Yousif, Osama
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Urban Change Detection Using Multitemporal SAR Images2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Multitemporal SAR images have been increasingly used for the detection of different types of environmental changes. The detection of urban changes using SAR images is complicated due to the complex mixture of the urban environment and the special characteristics of SAR images, for example, the existence of speckle. This thesis investigates urban change detection using multitemporal SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate effective methods for reduction of the speckle effect in change detection, (3) to investigate spatio-contextual change detection, (4) to investigate object-based unsupervised change detection, and (5) to investigate a new technique for object-based change image generation. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR and ENVISAT ASAR sensors were used for pixel-based change detection. For the object-based approaches, TerraSAR-X images were used.

    In Paper I, the unsupervised detection of urban change was investigated using the Kittler-Illingworth algorithm. A modified ratio operator that combines positive and negative changes was used to construct the change image. Four density function models were tested and compared. Among them, the log-normal and Nakagami ratio models achieved the best results. Despite the good performance of the algorithm, the obtained results suffer from the loss of fine geometric detail in general. This was a consequence of the use of local adaptive filters for speckle suppression. Paper II addresses this problem using the nonlocal means (NLM) denoising algorithm for speckle suppression and detail preservation. In this algorithm, denoising was achieved through a moving weighted average. The weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, principle component analysis (PCA) was used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the number of significant PCA components to be retained for weights computation and the required noise variance were proposed. The experimental results showed that the NLM algorithm successfully suppressed speckle effects, while preserving fine geometric detail in the scene. The analysis also indicates that filtering the change image instead of the individual SAR images was effective in terms of the quality of the results and the time needed to carry out the computation.

    The Markov random field (MRF) change detection algorithm showed limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle. To overcome this problem, Paper III utilizes the NLM theory to define a nonlocal constraint on pixels class-labels. The iterated conditional mode (ICM) scheme for the optimization of the MRF criterion function is extended to include a new step that maximizes the nonlocal probability model. Compared with the traditional MRF algorithm, the experimental results showed that the proposed algorithm was superior in preserving fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map.

    Paper IV investigates object-based unsupervised change detection using very high resolution TerraSAR-X images over urban areas. Three algorithms, i.e., Kittler-Illingworth, Otsu, and outlier detection, were tested and compared. The multitemporal images were segmented using multidate segmentation strategy. The analysis reveals that the three algorithms achieved similar accuracies. The achieved accuracies were very close to the maximum possible, given the modified ratio image as an input. This maximum, however, was not very high. This was attributed, partially, to the low capacity of the modified ratio image to accentuate the difference between changed and unchanged areas. Consequently, Paper V proposes a new object-based change image generation technique. The strong intensity variations associated with high resolution and speckle effects render object mean intensity unreliable feature. The modified ratio image is, therefore, less efficient in emphasizing the contrast between the classes. An alternative representation of the change data was proposed. To measure the intensity of change at the object in isolation of disturbances caused by strong intensity variations and speckle effects, two techniques based on the Fourier transform and the Wavelet transform of the change signal were developed. Qualitative and quantitative analyses of the result show that improved change detection accuracies can be obtained by classifying the proposed change variables. 

  • 137.
    Yousif, Osama A.
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Multitemporal Spaceborn SAR Data For Change Detection In Urban Areas: A Case Study In Shanghai2009In: Proceedings, ISPRS Virtual Changing Globe for Visualization and Analysis / [ed] Jianya Gong,Qiming Zhou, 2009Conference paper (Other academic)
    Abstract [en]

    The objective of this research is to perform automatic change detection within urban areas using multitemporal spaceborne SAR datain Shanghai. Two scenes of ENVISAT ASAR C-VV images were acquired in September, 2008 and one scene of ERS-2 SAR C-VVimage was acquired in September, 1999. A generalized version of Kittler Illingworth minimum-error thresholding algorithm, thattakes into account the non-Gaussianity of SAR images, was tested to automatically classify the SAR ratio image into two classes,change and no change. Two types of comparison operators were performed. First, the conventional ratio image was calculated in away that only increases in backscatter coefficient are detected. Second, a modified ratio operator that takes into accounts bothpositive and negative changes was also examined. Various probability density functions such as, Log normal, Generalized Gaussian,Nakagami ratio, and Weibull ratio were tested to model the distribution of the change and no change classes. An iterative refinementof the Log normal model is also applied to improve the resolution of the change map. The preliminary results showed that thisunsupervised change detection algorithm is very effective in detecting temporal changes in urban areas using SAR images. The bestchange detection result was obtained using Log normal model with modified ratio operator at 81.5%, which is over 10% high thanthat of the other three models tested. The initial findings indicated that change detection accuracy varies depending on how theassumed conditional class density function fits the histograms of change and no change classes.

  • 138.
    Yousif, Osama A.
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Object-based change detection in urban areas using multitemporal high resolution SAR images with unsupervised thresholding algorithms2016In: Remote Sensing and Digital Image Processing, Springer, 2016, p. 89-105Chapter in book (Refereed)
    Abstract [en]

    With the recent launches of optical and SAR systems that are capable of producing images in very high resolution, the quantification of temporal changes can be achieved with unprecedented level of details. However, very high resolution data presents new challenges and difficulties such as the strong intensity variations within land cover classes thus the noisy appearance of change map generated by pixelbased change detection. This has led to the development of object-based approaches that utilize image segmentation. For unsupervised change detection, on the other hand, automatic thresholding algorithms provided a simple yet effective technique to produce a binary change map. Thresholding techniques have been used successfully for pixel-based change detection using medium resolution SAR images. They have also been used for object-based change detection using high resolution optical imagery. However, they have not been tested in the context of object-based change detection using high resolution SAR images. Therefore, this chapter investigates the potential of several thresholding techniques for object-based unsupervised detection of urban changes using high resolution SAR images. To avoid the creation of sliver polygons, the multidate image segmentation strategy is adopted to produce image objects that are spectrally, spatially, and temporally homogeneous. A change image is generated by comparing objects multitemporal mean intensities using the modified ratio operator. To threshold the change image and generate a binary change map, three thresholding algorithms, i.e., the Kittler-Illingworth algorithm, the Otsu method, and the outlier detection technique, are tested and compared. Two multitemporal datasets consisting of TerraSAR-X images acquired over Beijing and Shanghai are used for evaluation. Quantitative and qualitative analyses reveal that the three algorithms achieved similar results. The three algorithms achieved Kappa coefficients around 0.6 for the Beijing dataset and 0.75 for the Shanghai datasets. The analysis also reveals the limitation of the mathematical comparison operator in accentuating the difference between the changed and the unchanged class, thus calls for the development of more sophisticated object-based change image generation mechanisms capable of reflecting all types of changes in the complex urban environment.

  • 139.
    Yousif, Osama
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    A novel approach for object-based change image generation using multitemporal high-resolution SAR images2017In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 38, no 7, p. 1765-1787Article in journal (Refereed)
    Abstract [en]

    Object-based change detection offers a unique approach for high-resolution images to capture meaningful detailed change information while suppressing noise in change detection results. In this approach, mean intensities of objects are commonly used as a feature and images comparison is carried out based on simple mathematical operations such as ratioing. The strong intensity variations within an object - a consequence of high spatial resolution - combined with synthetic aperture radar (SAR) image speckle degrade the accuracy of object mean intensity estimate, and consequently, affect the quality of the estimated object-based change image. A change quantification approach that takes into account the characteristics of high-resolution SAR images, that is, SAR speckle and the strong intensity variation, is proposed. By descending to the pixel level, a new representation of change data (i.e. the change signal) is proposed. With this representation, change quantification boils down to measuring the roughness of the change signal. Two techniques to assess the intensity of change at the object-level, based on Fourier and wavelet transforms (WT) of the change signal, are proposed. Their main advantages lie in their ability to capture the dominant change behaviour of the object, while being insusceptible to irrelevant disturbances. The proposed approach is evaluated using two multitemporal data sets of TerraSAR-X images acquired over Beijing and Shanghai. The qualitative and quantitative analyses of the results demonstrate the superior discrimination power of the proposed change variables compared with the object-based modified ratio (MR) and the absolute log ratio (LR) images.

  • 140.
    Yousif, Osama
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Fusion of SAR and optical data for unsupervised change detection: A case study in Beijing2017In: 2017 Joint Urban Remote Sensing Event, JURSE 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 7924636Conference paper (Refereed)
    Abstract [en]

    Change detection can either be carried out using multitemporal optical or synthetic aperture radar (SAR) images. Due to the different electromagnetic spectrum used, these two types of imagery provide different representations of the same physical reality. Change information extraction can benefit from the fusion of SAR and optical data. In this paper we investigate the fusion of SAR and optical for change detection application. Beijing, the capital of China that has experienced rapid urbanization, is selected as a case study. Two multitemporal datasets that consist of Landsat and SAR (ERS-2 and ENVISAT) images are used. An unsupervised classification framework that combines the virtues of the k-mean and SVM supervised classifier is proposed. Different fusion strategies are tested including fusion at the feature level and at the decision level. The analysis reveals that the best result can be obtained when the fusion of change information is carried out at the decision level.

  • 141.
    Yousif, Osama
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Object-based urban change detection using high resolution SAR images2015In: 2015 Joint Urban Remote Sensing Event, JURSE 2015, IEEE conference proceedings, 2015Conference paper (Refereed)
    Abstract [en]

    In this study, the unsupervised detection of urban changes, based on high-spatial resolution SAR imagery, is approached using the object-oriented paradigm. Multidate images segmentation strategy was adopted to avoid the creation of sliver polygon. Following segmentation, a change image was generated by comparing objects' mean intensities using a modified version of the traditional ratio operator. Three different unsupervised thresholding algorithms - that is, Kittler-Illingworth algorithm, Otsu method, and outlier detection technique - are used to threshold the change image and generate a binary change map. Two TerraSAR-X SAR images acquired over Shanghai in August, 2008, and September, 2011, were used to test the methods. The results indicate that, compared with pixel-based, the object-based approach helps in improving the quality of the produced change maps. The results also show that the three unsupervised thresholding algorithms performed equally well.

  • 142.
    Zdravkovic, Jelena
    et al.
    KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV. University of Gävle, Sweden .
    Östman, A.
    An agile method for automated provisioning of the geographical information in public services2008In: Proceedings of the Urban and Regional Data Management - UDMS Annual 2007, 2008, p. 319-331Conference paper (Refereed)
    Abstract [en]

    Lately we face broad emergence of the efforts for automation of business assets across organization boundaries in the form of electronic services (e-services). In the public services for spatial information provisioning, there is a strong need to improve efficiency of the current services by automating them in a uniform way across the country (Sweden) and further within the EU. However, a number of obstacles prohibits a comprehensive automation of the existing business services in a short run - for instance, the lack of schema standards for spatial concepts discourage digitalization of documents, whereas complexity of geographic maps depress their direct use by ordinary users. On the other side, ubiquity of the Web and increasing customer demands for obtaining services with the highest possible convenience, foster design of e-services in a short time. Following this, in this paper, we propose a method for an iterative approach to service automation. Using business goal analysis we identify the overall needs for the automation and then, in accordance to the present obstacles, we assess an appropriate level of automation. In a next iteration, i.e. when the goal model and/or obstacles are about to change, the service design is updated, and if needed, re-assessed from the economic perspective. The key aim of our approach is to foster a simple and step-wised automation of services for spatial information management, with possibilities for further iterative-based improvements. A case study from the domain of provisioning building permissions is used to ground and apply our proposed approach.

  • 143. Zeng, Q.
    et al.
    Gao, Liang
    Beijing University.
    Liu, R.
    Interferometric Phase Noise Filtering Based on Adaptive Optimized Wavelet Packets Transform2006In: Dragon Programme Mid-Term Results, Proceedings / [ed] Lacoste, H, 2006, p. 363-367Conference paper (Other academic)
    Abstract [en]

    Interferometric synthetic aperture radar (InSAR) has been used widely in investigation of surface deformation, such as earthquake, volcano, ground subsistence and landslides. But there are still some obstacles standing in the stage of the application. One of those is the phase noise in the interferogra. In particular, there are much noise in the inteferograms derived in steep topographic area with dense vegetation cover, such as the Three Gorges Area in China. This paper addresses the problem of phase noise filtering in InSAR. One difficulty of phase noise filtering of SAR interferometry is to reduce phase noise as much as possible while maintaining the phase information at the same time. The use of wavelet transform filtering is easy to ignore the high-frequency information, and make the image detail slur. One of the merits of wavelet packets transform is that most detail information in each frequency band will be analyzed. But over subtle decomposition in the high frequency bands, where noise is dominant, is apt to treat phase noise in those bands as signal. This would lead to erroneous result. In this paper, a phase noise filtering method based on adaptive optimized wavelet packets transform, or optimized tree-structured wavelet transform, is given out. By checking the correlation of wavelet coefficients in each wavelet scale, we decide whether each wavelet component in this scale should be decomposed further or not. According to such an adaptively constructed wavelet packets tree, complex phase image is decomposed. For the purpose of keeping the phase information, wavelet transform is executed in complex domain, and different threshold values are computed at each wavelet scale by using the intensity of their wavelet coefficients. Moreover, we have used an improved thresholding process method to try to overcome the disadvantages by both hard-thresholding and soft-thresholding methods.

  • 144. Zhang, H.
    et al.
    Ni, W.
    Yan, W.
    Xiang, Deliang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Wu, J.
    Yang, X.
    Bian, H.
    Registration of Multimodal Remote Sensing Image Based on Deep Fully Convolutional Neural Network2019In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 12, no 8, p. 3028-3042, article id 8730416Article in journal (Refereed)
    Abstract [en]

    Multimodal image registration is the fundamental technique for scene analysis with series remote sensing images of different spectrum region. Due to the highly nonlinear radiometric relationship, it is quite challenging to find common features between images of different modal types. This paper resorts to the deep neural network, and tries to learn descriptors for multimodal image patch matching, which is the key issue of image registration. A Siamese fully convolutional network is set up and trained with a novel loss function, which adopts the strategy of maximizing the feature distance between positive and hard negative samples. The two branches of the Siamese network are connected by the convolutional operation, resulting in the similarity score between the two input image patches. The similarity score value is used, not only for correspondence point location, but also for outlier identification. A generalized workflow for deep feature based multimodal RS image registration is constructed, including the training data curation, candidate feature point generation, and outlier removal. The proposed network is tested on a variety of optical, near infrared, thermal infrared, SAR, and map images. Experiment results verify the superiority over other state-of-the-art approaches.

  • 145.
    Zhang, Han
    et al.
    Northwest Inst Nucl Technol, Remote Sensing Data Proc Lab, Xian 710024, Shaanxi, Peoples R China..
    Ni, Weiping
    Northwest Inst Nucl Technol, Xian 710024, Shaanxi, Peoples R China.;Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China..
    Yan, Weidong
    Northwest Inst Nucl Technol, Remote Sensing Data Proc Lab, Xian 710024, Shaanxi, Peoples R China..
    Xiang, Deliang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Wu, Junzheng
    Northwest Inst Nucl Technol, Remote Sensing Data Proc Lab, Xian 710024, Shaanxi, Peoples R China..
    Yang, Xiaoliang
    Northwest Inst Nucl Technol, Remote Sensing Data Proc Lab, Xian 710024, Shaanxi, Peoples R China..
    Bian, Hui
    Northwest Inst Nucl Technol, Remote Sensing Data Proc Lab, Xian 710024, Shaanxi, Peoples R China..
    Registration of Multimodal Remote Sensing Image Based on Deep Fully Convolutional Neural Network2019In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 12, no 8, p. 3028-3042Article in journal (Refereed)
    Abstract [en]

    Multimodal image registration is the fundamental technique for scene analysis with series remote sensing images of different spectrum region. Due to the highly nonlinear radiometric relationship, it is quite challenging to find common features between images of different modal types. This paper resorts to the deep neural network, and tries to learn descriptors for multimodal image patch matching, which is the key issue of image registration. A Siamese fully convolutional network is set up and trained with a novel loss function, which adopts the strategy of maximizing the feature distance between positive and hard negative samples. The two branches of the Siamese network are connected by the convolutional operation, resulting in the similarity score between the two input image patches. The similarity score value is used, not only for correspondence point location, but also for outlier identification. A generalized workflow for deep feature based multimodal RS image registration is constructed, including the training data curation, candidate feature point generation, and outlier removal. The proposed network is tested on a variety of optical, near infrared, thermal infrared, SAR, and map images. Experiment results verify the superiority over other state-of-the-art approaches.

  • 146.
    Zhang, Qian
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Analysis of landscape dynamics in Shanghai using landscape metrics: effects of spatial resolution2008In: XXI Congress of International Society for Photogrammetry and Remote Sensing. july, 2008. Beijing, China, 2008Conference paper (Refereed)
    Abstract [en]

    It is increasingly evident that urban sprawl leads to dramatic changes in landscape patterns and thus changes in ecosystem functioning. Analysis of the landscape patterns and their dynamic under urbanization is of great importance for sustainable development, especially in cities with significant changes like Shanghai. The objective of this research is to illustrate the landscape dynamic under the urbanization process in a selected test area of Shanghai in 1991, 1998 and 2007 using multitemtopal remote sensing and landscape metrics; and to determine the optimal resolution suitable for this case study. Preliminary results show that it is a quick and executable way to assessing the impact of urban sprawl on landscape dynamic using remote sensing data and landscape matrices; and the optimal resolution for the case study is 10-30 meters.

     

  • 147.
    Zhang, Qian
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Evaluation of urban expansion and its impact on surface temperature in Beijing, China2011In: IEEE GRSS and ISPRS Joint Urban Remote Sensing Event, JURSE 2011, 2011, p. 357-360Conference paper (Refereed)
    Abstract [en]

    This paper reports an investigation into the urban expansion in Beijing during 2004-2009 and its impact on land surface temperature (LST), as well as the relationship between LST and NDVI. LST and landuse/land-cover (LULC) data were extracted from Landsat Thematic Mapper (TM) images acquired on September 8, 2004 and September 22, 2009. The spatial pattern of LST before and after the 2008 Summer Olympic Games in Beijing were compared. The change detection results revealed that the increase of the built-up areas of approximately 139.19km 2 during 2004 to 2009 and this accounted for approximately 4% of the study area. The spatial pattern of LST was non-symetrical and non-concentric. The high temperature zones clustered towards the south of the central axis and extend to the fifth ring road in 2004, while this situation was relieved in 2009. The correlation analysis indicated that LST values tend to negatively correlate with NDVI in both years with the coefficients of -0.751 and -0.732 correspondingly. Moreover, the overall changes of NDVI accounted for approximately 40% of the LST changes.

  • 148.
    Zhang, Qian
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Monitoring impervious surface sprawl in Shanghai using tasseled cap transformation of landsat data2010Conference paper (Other academic)
    Abstract [en]

    Transformation from non-impervious surface to impervious surface changes the landscapes and also changes the ecological and environmental conditions. Detecting impervious surface growth is vital to monitoring urban development and supporting sustainable city planning. The objective of this research is to conduct detection of impervious surface sprawl using tasseled cap transformation within the conceptual framework of Vegetation-Impervious surface-Soil (V-I-S) model. Landsat-3 MSS images on August 4, 1979 and Gap-filled Landsat-7 ETM+ images on May 22, 2009, covering the Greater Shanghai Area, were used in the case study. The results demonstrated that direct change detection using variables derived from tasseled cap transformation was effective for monitoring impervious surface sprawl. The variables derived from tasseled cap transformation have the potential to link to the components of the V-I-S model. The Greater Shanghai Area experienced high-speed impervious surface sprawl over the past 30 years at the average speed of 38.84km2/year.

  • 149.
    Zhang, Qian
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Hu, Yunfeng
    Institute of Geographic Science and Natural Resorces Research, Chinese Academy of Sciences, China.
    Liu, Jiyuan
    Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, China.
    The Trajectories of Urban Land and Industrial Land in Shanghai over the Past 30 Years2009In: 2009 JOINT URBAN REMOTE SENSING EVENT, VOLS 1-3, New York: IEEE , 2009, p. 1022-1028Conference paper (Refereed)
    Abstract [en]

    With regional economy development and the changes of urban development strategies, Shanghai experienced a distinct land use change process during the past 30 year. To explore the spatial pattern and temporal dynamics of urban land and industrial land over the past years, two independent datasets from multitemporal satellite images based on interpretation with computer-aids and object-oriented classification methods were employed in this study. Preliminary study indicated that Shanghai's urban land experienced continuous increases over the past 30 years. The total urban area of Shanghai increased from 146.1km(2) to 1121.3km(2) during 1979-2007, with an annual urban expansion area of 34.8km(2) per year. The rate of urban expansion, however, was not homogeneous spatially and temporally. The expanded area in the Puxi region was 5.23 times of its original area while that of Pudong region was 19.94 times of its original area. Within the extent of 1979's urban land distribution, the told area of industrial land and warehouses way 14.7 km(2) in 1966 and 16.8 km(2) in 2007, with the ratio of the industrial land to the whole territory increased from 10.1% to 11.5% The sizes of industrial land patches within the boundary decreased with the total industrial land area and the number of industrial patches increased. Moreover, the average size of industrial land in the Pudong region (1.6-2.0ha) was larger than that in the Puxi region (1.1-1.8ha). The trajectories of urban land and industrial land in Shanghai can he well explained by the policies of the national and the local government.

  • 150.
    Zhang, Qian
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Liu, Jiyuan
    Hu, Yunfeng
    Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China2011In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 35, no 2, p. 126-139Article in journal (Refereed)
    Abstract [en]

    This research investigates the potential of an integrated Markov chain analysis and cellular automata model to better understand the dynamics of Shanghai's urban growth. The model utilizes detailed land cover categories to simulate and assess landscape changes under three different scenarios, i.e., baseline, Service Oriented Center, and Manufacturing Dominant Center scenarios. In the study, multi-temporal land use datasets, derived from remotely-sensed images from 1995, 2000, and 2005, were used for simulation and validation. Urban growth patterns and processes were then analyzed and compared with the aid of landscape metrics. This research represents the first scenario-based simulations of the future growth of Shanghai, and is one of the few studies to use landscape metrics to analyze urban scenario-based simulation results with detailed land use categories. The results indicate that the future expansion of both high-density and low-density residential/commercial zones is always located around existing built-up urban areas or along existing transportation lines. In contrast to the baseline and Service Oriented Center scenarios, industrial land under the Manufacturing Dominant Center scenario in 2015 and 2025 will form industrial parks or industrial belts along the transportation channels from Shanghai to Nanjing and Hangzhou. The study's approach, which combines scenario-based urban simulation modeling and landscape metrics, is shown to be effective in representing, understanding, and predicting the spatial-temporal dynamics and patterns of urban evolution, including urban expansion trends. (C) 2010 Elsevier Ltd. All rights reserved.

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