This article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain-interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Donana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.

The overall objective of this research is to evaluate multitemporal spaceborne SAR and optical data for urban land cover mapping and urbanization monitoring. Multitemporal Sentinel-1A SAR and historical ERS SAR and ENVISAT ASAR data as well as HJ-1B multispectral data were acquired in Beijing, Chendgdu and Nanchang, China where rapid urbanization has taken place. KTHSEG, a novel object-based classification method is adopted for urban land cover mapping while KTH-Pavia Urban Extractor, a robust algorithm is improved for urban extent extraction and urbanization monitoring. The research demonstrates that, for urban land cover classification, the fusion of multitemporal SAR and optical data is superior to SAR or optical data alone. The second best classification result is achieved using fusion of 4-date SAR and one HJ-1B image. The results indicate that carefully selected multitemporal SAR dataset and its fusion with optical data could produce nearly as good classification accuracy as the whole multitemporal dataset. The results also show that KTH-SEG, the edge-aware region growing and merging segmentation algorithm, is effective for classification of SAR, optical and their fusion. KTH-SEG outperforms eCognition, the commonly used commercial software, for image segmentation and classification of linear features. For Urban extent extraction, single-date and multitemporal SAR data including ERS SAR, ENVISAT ASAR and Sentinel-1A SAR achieved very promising results in all study areas using the improved KTH-Pavia Urban Extractor. The results showed that urban areas as well as small towns and villages could be well extracted using multitemporal Sentinel-1A SAR data while major urban areas could be well extracted using a single-date single-polarization SAR image. The results clearly demonstrate that multitemporal SAR data are cost- and time-effective way for monitoring spatiotemporal patterns of urbanization.

In this paper, Sentinel-1A SAR data were evaluated for urban extent extraction using the KTH-Pavia Urban Extractor. The methodology is based on texture measures and spatial statistics as well as decision level fusion. Sentinel-1A data in Stripmap mode (SM) or Interferometric Wide Swath mode (IW) were acquired in five cities around the world. For two European cities Milan, Italy and Stockholm, Sweden, accuracy assessments were performed by comparing the extracted urban areas with the Urban Atlas in 2010. The preliminary results show that the Sentinel-1A Stripmap mode is very suitable for urban extraction, reaching an agreement of more than 83%. The Interferometric Wideswath mode shows potential as well reaching an agreement of 77%, but urban extraction over Stockholm had high omission errors in low density built up areas.

With more than half of the world population now living in cities and 1.4 billion more people expected to move into cities by 2030, urban areas pose significant challenges on local, regional and global environment. Timely and accurate information on spatial distributions and temporal changes of urban areas are therefore needed to support sustainable development and environmental change research. The objective of this research is to evaluate spaceborne SAR data for improved global urban mapping using a robust processing chain, the KTH-Pavia Urban Extractor. The proposed processing chain includes urban extraction based on spatial indices and Grey Level Co-occurrence Matrix (GLCM) textures, an existing method and several improvements i.e., SAR data preprocessing, enhancement, and post-processing. ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-VV data at 30m resolution were selected over 10 global cities and a rural area from six continents to demonstrated robustness of the improved method. The results show that the KTH-Pavia Urban Extractor is effective in extracting urban areas and small towns from ENVISAT ASAR data and built-up areas can be mapped at 30m resolution with very good accuracy using only one or two SAR images. These findings indicate that operational global urban mapping is possible with spaceborne SAR data, especially with the launch of Sentinel-1 that provides SAR data with global coverage, operational reliability and quick data delivery.

The objective of this licentiate thesis is to develop novel algorithms and improve existing methods for urban land cover mapping and urban extent extraction using multi-temporal remote sensing imagery. Past studies have demonstrated that synthetic aperture radar (SAR) have very good properties for the analysis of urban areas, the synergy of SAR and optical data is advantageous for various applications. The specific objectives of this research are:

1. To develop a novel edge-aware region-growing and -merging algorithm, KTH-SEG, for effective segmentation of SAR and optical data for urban land cover mapping;

2. To evaluate the synergistic effects of multi-temporal ENVISAT ASAR and HJ-1B multi-spectral data for urban land cover mapping;

3. To improve the robustness of an existing method for urban extent extraction by adding effective pre- and post-processing.

ENVISAT ASAR data and the Chinese HJ-1B multispectral , as well as TerraSAR-X data were used in this research. For objectives 1 and 2 two main study areas were chosen, Beijing and Shanghai, China. For both sites a number of multitemporal ENVISAT ASAR (30m C-band) scenes with varying image characteristics were selected during the vegetated season of 2009. For Shanghai TerraSAR-X strip-map images at 3m resolution X-band) were acquired for a similar period in 2010 to also evaluate high resolution X-band SAR for urban land cover mapping. Ten  major landcover classes were extracted including high density built-up, low density built-up, bare field, low vegetation, forest, golf course, grass, water, airport runway and major road.

For Objective 3, eleven globally distributed study areas where chosen, Berlin, Beijing, Jakarta, Lagos, Lombardia (northern Italy), Mexico City, Mumbai, New York City, Rio de Janeiro, Stockholm and Sydney. For all cities ENVISAT ASAR imagery was acquired and for cities in or close to mountains even SRTM digital elevation data.

The methodology of this thesis includes two major components, KTH-SEG and KTH-Pavia Urban Extractor. KTH-SEG is an edge aware region-growing and -merging algorithm that utilizes both the benefit of finding local high frequency changes as well as determining robustly homogeneous areas of a low frequency in local change. The post-segmentation classification is performed using support vector machines. KTH-SEG was evaluated using multitemporal, multi-angle, dual-polarization ASAR data and multispectral HJ-1B data as well as TerraSAR-X data. The KTH-Pavia urban extractor is a processing chain. It includes: Geometrical corrections, contrast enhancement, builtup area extraction using spatial stastistics and GLCM texture features, logical operator based fusion and DEM based mountain masking.

For urban land cover classification using multitemporal ENVISAT ASAR data, the results showed that KTH-SEG achieved an overall accuracy of almost 80% (0.77 Kappa ) for the 10 urban land cover classes both Beijign and Shanghai, compared to eCognition results of 75% (0.71 Kappa) In particular the detection of small linear features with respect to the image resolution such as roads in 30m resolved data went well with 83% user accuracy from KTH-SEG versus 57% user accuracy using the segments derived from eCognition. The other urban classes which in particular in SAR imagery are characterized by a high degree of heterogeneity were classified superiorly by KTH-SEG. ECognition in general performed better on vegetation classes such as grass, low vegetation and forest which are usually more homogeneous.

It is was also found that the combination of ASAR and HJ-1B optical data was beneficial, increasing the final classification accuracy by at least 10% compared to ASAR or HJ-1B data alone. The results also further confirmed that a higher diversity of SAR type images is more important for the urban classification outcome. However, this is not the case when classifying high resolution TerraSAR-X strip-map imagery. Here the different image characteristics of different look angles, and orbit orientation created more confusion mainly due to the different layover and foreshortening effects on larger buildings. The TerraSAR-X results showed also that accurate urban classification can be achieved using high resolution SAR data alone with almost 84% for  eight classes around the Shanghai international Airport (high and low density built-up were not separated as well as roads and runways).

For urban extent extraction, the results demonstrated that built-up areas can be effectively extracted using a single ENVISAT ASAR image in 10 global cities reaching overall accuracies around 85%, compared to 75% of MODIS urban class and 73% GlobCover Urban class. Multitemporal ASAR can improve the urban extraction results by 5-10% in Beijing. Mountain masking applied in Mumbai and Rio de Janeiro increased the accuracy by 3-5%.The research performed in  this thesis has contributed to the remote sensing community by providing algorithms and methods for both extracting urban areas and identifying urban land cover in a more detailed fashion. 

The potential of high-resolution optical satellite images for mapping of ecologically important urban space is investigated in this study. Both a GeoEye-1 and a Landsat 8 scene over central Shanghai were first segmented by two different algorithms and then classified into seven urban classes by SVM. Shadows in the pan-sharpened GeoEye-1 image were masked out and replaced by the corresponding pan-sharpened classified Landsat 8 image. Largest confusions occurred between sealed and permeable but non-vegetated surfaces, and between low-rise residential and high-rise commercial buildings. Based on the classification result, ecosystem service balances, supply and demand was modelled for each particular land cover class. Classification accuracies of 88% and 91% could be reached, indicating the suitability of the underlying data and method for this application domain. The KTH-SEG segmentation algorithm slightly outperformed the one implemented in eCognition. The highest supply of ecosystem services was found in water bodies whereas high-rise built-up areas revealed largest demands.

TerraSAR X data has been analyzed for its suitability of urban land cover mapping using our recently developed object based image analysis tool KTH-SEG, which is based on an edge aware region growing and merging algorithm and a support vector machine classifier. Classification results over the Shanghai International Airport area using 8 classes, Water, Grass, Roads, Buildings, Crops, Forest, Bare Crops and Green Houses have proven with an overall accuracy just shy of 84% that this is very well the case. It has further been investigated which segment sizes and image configuration yield the best results.

The objectives of this research are to develop robust methods for segmentation of multitemporal synthetic aperture radar (SAR) and optical data and to investigate the fusion of multitemporal ENVISAT advanced synthetic aperture radar (ASAR) and Chinese HJ-1B multispectral data for detailed urban land-cover mapping. Eight-date multiangle ENVISAT ASAR images and one-date HJ-1B charge-coupled device image acquired over Beijing in 2009 are selected for this research. The edge-aware region growing and merging (EARGM) algorithm is developed for segmentation of SAR and optical data. Edge detection using a Sobel filter is applied on SAR and optical data individually, and a majority voting approach is used to integrate all edge images. The edges are then used in a segmentation process to ensure that segments do not grow over edges. The segmentation is influenced by minimum and maximum segment sizes as well as the two homogeneity criteria, namely, a measure of color and a measure of texture. The classification is performed using support vector machines. The results show that our EARGM algorithm produces better segmentation than eCognition, particularly for built-up classes and linear features. The best classification result (80%) is achieved using the fusion of eight-date ENVISAT ASAR and HJ-1B data. This represents 5%, 11%, and 14% improvements over eCognition, HJ-1B, and ASAR classifications, respectively. The second best classification is achieved using fusion of four-date ENVISAT ASAR and HJ-1B data (78%). The result indicates that fewer multitemporal SAR images can achieve similar classification accuracy if multitemporal multiangle dual-look-direction SAR data are carefully selected.

The paper aims to develop image segmentation algorithms for classification of multi-sensor data in urban areas. For this purpose an algorithm called KTHSEG has been developed using an edge-aware region growing and merging algorithm. Four-date ENVISAT ASAR C-HH data and one-date HJ-1B covering the city of Shanghai acquired during the vegetation season of 2009 were selected this research. The results show that the segmentation algorithm is effective for urban land cover classification using SAR and optical data. The results also confirm that the fusion of SAR and optical data is beneficial for urban land cover mapping. Further, the study showed that the combination of one SAR and one optical scene is enough to achieve good results and the addition of multitemporal SAR data from the same beam mode does not improve classification accuracy.