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Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping
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, Geodesy and Geoinformatics.ORCID iD: 0000-0003-4434-7244
2013 (English)In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 51, no 4, 1998-2006 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. Vol. 51, no 4, 1998-2006 p.
Keyword [en]
Edge-aware region growing and merging (EARGM), ENVISAT advanced synthetic aperture radar (SAR) (ASAR), fusion, HJ-1B, multitemporal, segmentation, urban land cover
National Category
URN: urn:nbn:se:kth:diva-123430DOI: 10.1109/TGRS.2012.2236560ISI: 000318426400012ScopusID: 2-s2.0-84875675131OAI: diva2:626901

QC 20130610

Available from: 2013-06-10 Created: 2013-06-10 Last updated: 2014-06-25Bibliographically approved
In thesis
1. Multitemporal Remote Sensing for Urban Mapping using KTH-SEG and KTH-Pavia Urban Extractor
Open this publication in new window or tab >>Multitemporal Remote Sensing for Urban Mapping using KTH-SEG and KTH-Pavia Urban Extractor
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. ix, 51 p.
TRITA-SOM, ISSN 1653-6126 ; 2014:08
KTH-SEG, ASAR, HJ-1B, Urban Land Cover Mapping, OBIA, Segmentation, Image Classification, KTH-Pavia Urban Extractor, Urban Extent
National Category
Other Computer and Information Science
Research subject
Geodesy and Geoinformatics
urn:nbn:se:kth:diva-147159 (URN)978-91-7595-188-1 (ISBN)
2014-06-12, Seminarrum 5055, Drottning Kristinas Väg 30, Stockholm, 10:00 (English)

QC 20140625

Available from: 2014-06-25 Created: 2014-06-23 Last updated: 2014-06-25Bibliographically approved

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