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Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Urban land cover mapping represents one of the most important remote sensing applications in the context of rapid global urbanization. In recent years, high resolution spaceborne Polarimetric Synthetic Aperture Radar (PolSAR) has been increasingly used for urban land cover/land-use mapping, since more information could be obtained in multiple polarizations and the collection of such data is less influenced by solar illumination and weather conditions. 

The overall objective of this research is to develop effective methods to extract accurate and detailed urban land cover information from spaceborne PolSAR data. Six RADARSAT-2 fine-beam polarimetric SAR and three RADARSAT-2 ultra-fine beam SAR images were used. These data were acquired from June to September 2008 over the north urban-rural fringe of the Greater Toronto Area, Canada. The major landuse/land-cover classes in this area include high-density residential areas, low-density residential areas, industrial and commercial areas, construction sites, roads, streets, parks, golf courses, forests, pasture, water and two types of agricultural crops.

In this research, various polarimetric SAR parameters were evaluated for urban land cover mapping. They include the parameters from Pauli, Freeman and Cloude-Pottier decompositions, coherency matrix, intensities of each polarization and their logarithms.  Both object-based and pixel-based classification approaches were investigated. Through an object-based Support Vector Machine (SVM) and a rule-based approach, efficiencies of various PolSAR features and the multitemporal data combinations were evaluated. For the pixel-based approach, a contextual Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) and a modified Multiscale Pappas Adaptive Clustering (MPAC), contextual information was explored to improve the mapping results. To take full advantages of alternative PolSAR distribution models, a rule-based model selection approach was put forward in comparison with a dictionary-based approach.  Moreover, the capability of multitemporal fine-beam PolSAR data was compared with multitemporal ultra-fine beam C-HH SAR data. Texture analysis and a rule-based approach which explores the object features and the spatial relationships were applied for further improvement.

Using the proposed approaches, detailed urban land-cover classes and finer urban structures could be mapped with high accuracy in contrast to most of the previous studies which have only focused on the extraction of urban extent or the mapping of very few urban classes. It is also one of the first comparisons of various PolSAR parameters for detailed urban mapping using an object-based approach. Unlike other multitemporal studies, the significance of complementary information from both ascending and descending SAR data and the temporal relationships in the data were the focus in the multitemporal analysis. Further, the proposed novel contextual analyses could effectively improve the pixel-based classification accuracy and present homogenous results with preserved shape details avoiding over-averaging. The proposed contextual SEM algorithm, which is one of the first to combine the adaptive MRF and the modified MPAC, was able to mitigate the degenerative problem in the traditional EM algorithms with fast convergence speed when dealing with many classes. This contextual SEM outperformed the contextual SVM in certain situations with regard to both accuracy and computation time. By using such a contextual algorithm, the common PolSAR data distribution models namely Wishart, G0p, Kp and KummerU were compared for detailed urban mapping in terms of both mapping accuracy and time efficiency. In the comparisons, G0p, Kp and KummerU demonstrated better performances with higher overall accuracies than Wishart. Nevertheless, the advantages of Wishart and the other models could also be effectively integrated by the proposed rule-based adaptive model selection, while limited improvement could be observed by the dictionary-based selection, which has been applied in previous studies. The use of polarimetric SAR data for identifying various urban classes was then compared with the ultra-fine-beam C-HH SAR data. The grey level co-occurrence matrix textures generated from the ultra-fine-beam C-HH SAR data were found to be more efficient than the corresponding PolSAR textures for identifying urban areas from rural areas. An object-based and pixel-based fusion approach that uses ultra-fine-beam C-HH SAR texture data with PolSAR data was developed. In contrast to many other fusion approaches that have explored pixel-based classification results to improve object-based classifications, the proposed rule-based fusion approach using the object features and contextual information was able to extract several low backscatter classes such as roads, streets and parks with reasonable accuracy.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. , x, 95 p.
Series
Trita-SOM , ISSN 1653-6126 ; 2012:18
Keyword [en]
RADARSAT-2, Spaceborne, Polarimetric SAR, Urban Land cover, Object-based Rule-based Classification, Support Vector Machines, Contextual, Stochastic Expectation-Maximization, Markov Random Field.
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-104762ISBN: 978-91-7501-535-4 (print)OAI: oai:DiVA.org:kth-104762DiVA: diva2:567158
Public defence
2012-11-23, Sal D3, Lindstedtsvägen 5, entréplan, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20121112

Available from: 2012-11-12 Created: 2012-11-12 Last updated: 2012-11-12Bibliographically approved
List of papers
1. Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach
Open this publication in new window or tab >>Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach
2013 (English)In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 34, no 1, 1-26 p.Article in journal (Refereed) Published
Abstract [en]

We have investigated multi-temporal polarimetric synthetic aperture radar (SAR) data for urban land-cover classification using an object-based support vector machine (SVM) in combinations of rules. Six-date RADARSAT-2 high-resolution polarimetric SAR data in both ascending and descending passes were acquired in the rural-urban fringe of the Greater Toronto Area during the summer of 2008. The major land-use/land-cover classes include high-density residential areas, low-density residential areas, industrial and commercial areas, construction sites, parks, golf courses, forests, pasture, water, and two types of agricultural crops. Various polarimetric SAR parameters were evaluated for urban land-cover mapping and they include the parameters from Pauli, Freeman and Cloude-Pottier decompositions, the coherency matrix, intensities of each polarization, and their logarithm forms. The multi-temporal SAR polarimetric features were classified first using an SVM classifier. Then specific rules were developed to improve the SVM classification results by extracting major roads and streets using shape features and contextual information. For the comparison of the polarimetric SAR parameters, the best classification performance was achieved using the compressed logarithmic filtered Pauli parameters. For the evaluation of the multi-temporal SAR data set, the best classification result was achieved using all six-date data (kappa = 0.91), while very good classification results (kappa = 0.86) were achieved using only three-date polarimetric SAR data. The results indicate that the combination of both the ascending and the descending polarimetric SAR data with an appropriate temporal span is suitable for urban land-cover mapping.

National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-103959 (URN)10.1080/01431161.2012.700133 (DOI)000308994100001 ()2-s2.0-84868092003 (Scopus ID)
Note

QC 20121029

Available from: 2012-10-29 Created: 2012-10-25 Last updated: 2017-12-07Bibliographically approved
2. An Adaptive Contextual SEM Algorithm for Urban Land Cover Mapping Using Multitemporal High-Resolution Polarimetric SAR Data
Open this publication in new window or tab >>An Adaptive Contextual SEM Algorithm for Urban Land Cover Mapping Using Multitemporal High-Resolution Polarimetric SAR Data
2012 (English)In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 5, no 4, 1129-1139 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a semi-supervised Stochastic Expectation-Maximization (SEM) algorithm for detailed urban land cover mapping using multitemporal high-resolution polarimetric SAR (PolSAR) data. By applying an adaptive Markov Random Field (MRF) with the spatially variant Finite Mixture Model (SVFMM), spatial-temporal contextual information could be effectively explored to improve the mapping accuracy with homogenous results and preserved shape details. Further, a learning control scheme was proposed to ensure a robust semi-supervised mapping process thus avoiding the undesired class merges. Four-date RADARSAT-2 polarimetric SAR data over the Greater Toronto Area were used to evaluate the proposed method. Common PolSAR distribution models such as Wishart, G0p, Kp and KummerU were compared through this contextual SEM algorithm for detailed urban land cover mapping. Comparisons with Support Vector Machine (SVM) were also conducted to assess the potential of our parametric approach. The results show that the Kp, G0p and KummerU models could generate better urban land cover mapping results than the Wishart model and SVM.

Keyword
Adaptive MRF, polarimetric SAR, semi-supervised classification, urban land cover
National Category
Geosciences, Multidisciplinary
Identifiers
urn:nbn:se:kth:diva-101120 (URN)10.1109/JSTARS.2012.2201448 (DOI)000306922100007 ()2-s2.0-84864740597 (Scopus ID)
Note

QC 20120827

Available from: 2012-08-27 Created: 2012-08-23 Last updated: 2017-12-07Bibliographically approved
3. A Novel Contextual Classification Algorithm for Multitemporal Polarimetric SAR Data
Open this publication in new window or tab >>A Novel Contextual Classification Algorithm for Multitemporal Polarimetric SAR Data
2014 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 11, no 3, 681-685 p.Article in journal (Refereed) Published
Abstract [en]

This letter presents a pixel-based contextual classification algorithm by integrating a multiscale modified Pappas adaptive clustering (mMPAC) and an adaptive Markov random field (AMRF) into the stochastic expectation-maximization process for urban land cover mapping using multitemporal polarimetric synthetic aperture radar (PolSAR) data. This algorithm can effectively explore spatiotemporal contextual information to improve classification accuracy. Using the mMPAC, the problem caused by the class feature variation could be mitigated. Using the AMRF, shape details could be preserved from overaveraging that often occurs in many nonadaptive contextual approaches. Six-date RADARSAT-2 PolSAR data over the Greater Toronto Area were used for evaluation. The results show that this algorithm outperformed the support vector machine in producing homogeneous and detailed land cover classification in a complex urban environment with high accuracy.

Keyword
Contextual classification, modified Pappas adaptive clustering (MPAC), Markov random field (MRF), polarimetric synthetic aperture radar (PolSAR), stochastic expectation-maximization (SEM), urban land cover
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-104759 (URN)10.1109/LGRS.2013.2274815 (DOI)000332182600020 ()
Note

QC 20140328. Updated from manuscript to article in journal.

Available from: 2012-11-12 Created: 2012-11-12 Last updated: 2017-12-07Bibliographically approved
4. Multitemporal Polarimetric RADARSAT-2 SAR Data for Urban Land Cover Mapping Through a Dictionary-based and a Rule-based Model Selection in a Contextual SEM Algorithm
Open this publication in new window or tab >>Multitemporal Polarimetric RADARSAT-2 SAR Data for Urban Land Cover Mapping Through a Dictionary-based and a Rule-based Model Selection in a Contextual SEM Algorithm
2013 (English)In: Canadian journal of remote sensing, ISSN 0703-8992, E-ISSN 1712-7971, Vol. 39, no 2, 138-151 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a dictionary-based and a rule-based model selection approach in an adaptive contextual semi-supervised algorithm for improving urban land cover classification using high-resolution multitemporal RADARSAT-2 polarimetric SAR (PolSAR) data.  Six-date PolSAR data were acquired during June to September, 2008 over the Greater Toronto Area. Contextual information and the capabilities of different PolSAR distribution models were explored by the spatially variant Finite Mixture Model (FMM) with an adaptive Markov Random Field (MRF) in a Stochastic Expectation-Maximization (SEM) algorithm. This algorithm can obtain homogenous results while preserving shape details in the complex urban environment with high accuracy. Commonly used PolSAR distribution models such as Wishart, G0p, Kp and KummerU were compared through the proposed approaches for urban land cover mapping. According to a Goodness-of-fit test based on Mellin transformation, accurate PolSAR distribution model could be selected with the dictionary-based classification. However, the results showed that improvement by the dictionary-based approach was limited. Therefore, further improvements were expected by exploring expert knowledge. The initial results showed that G0p and KummerU performed better for distinguishing between low density built-up areas and forest. G0p, Kp and KummerU are better for the low scattering classes. The Wishart model has superior capacity in separating high density built-up areas and the adjacent roads. Based on such knowledge, a set of rules were developed to integrate the advantages of alternative models. Significant improvement on the overall classification accuracy could be observed by such rule-based approach. The biggest improvement was achieved using HD-Road rule on G0p model with the best overall classification accuracy at 89.99% (kappa: 0.87). This represented 4.1% (kappa: 0.045) improvement over that of G0p without model selection.

Keyword
RADARSAT-2, Polarimetric SAR, Urban Land Cover, Markov Random Field, Stochastic Expectation-Maximization, Rule-based Approach, Dictionary-based Approach
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-104761 (URN)10.5589/m13-019 (DOI)000328372900004 ()2-s2.0-84882238279 (Scopus ID)
Note

Updated from "Manuscript" to "Article" QC 20131211

Available from: 2012-11-12 Created: 2012-11-12 Last updated: 2017-12-07Bibliographically approved
5. RADARSAT-2 fine-beam polarimetric and ultra-fine beam SAR data for urban land cover mapping: Comparison and Synergy
Open this publication in new window or tab >>RADARSAT-2 fine-beam polarimetric and ultra-fine beam SAR data for urban land cover mapping: Comparison and Synergy
(English)Manuscript (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.  

Keyword
Adaptive MRF, polarimetric SAR, texture, urban land cover.
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-104760 (URN)
Note

QS 2012

Available from: 2012-11-12 Created: 2012-11-12 Last updated: 2012-11-12Bibliographically approved

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CiteExportLink to record
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