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Multitemporal RADARSAT-2 ultra-fine beam SAR data for urban land cover classification
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi. (Geoinformatics)
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi. (Geoinformatics)
2012 (English)In: Canadian journal of remote sensing, ISSN 0703-8992, E-ISSN 1712-7971, Vol. 38, no 1, 1-11 p.Article in journal (Refereed) Published
Abstract [en]

High-resolution optical satellite images have been widely used to update land cover information and monitor changes in urban areas. Several spaceborne synthetic aperture radar (SAR) systems are now providing SAR imagery with a spatial resolution comparable to high-resolution optical systems. Although SAR data is more reliably available than optical data, it takes more effort to employ high-resolution SAR imagery for urban applications owing to the difficulty in interpreting the complex content in SAR imagery over urban areas. The objective of this research was to develop effective object-based and rule-based methods for classification of high-resolution SAR imagery over urban areas. Multitemporal RADARSAT-2 ultra-fine beam C-HH SAR images with a pixel spacing of 1.56 m were acquired over the north part of the Greater Toronto Area during June to September in 2008. The SAR images were preprocessed and then segmented by means of a multiresolution segmentation algorithm. A range of spectral, geometrical, and textural features were selected and calculated for image objects. The image objects were classified based on these features using support vector machines (SVM). Compared with the nearest neighbor classifier, the object-based SVM produced much higher urban land cover classification accuracy (Kappa 0.43 vs. 0.63). The SVM classification result was then improved by developing specific rules to resolve the confusion among some classes. The final result indicated that the proposed methods could achieve a satisfactory overall accuracy (81.8%) for urban land cover classification using very high-resolution RADARSAT-2 SAR imagery.

Place, publisher, year, edition, pages
2012. Vol. 38, no 1, 1-11 p.
Keyword [en]
RADARSAT-2 SAR, urban land cover, object-based classification, support vector machines
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-27206DOI: 10.5589/m12-008ISI: 000306218900001Scopus ID: 2-s2.0-84865785563OAI: oai:DiVA.org:kth-27206DiVA: diva2:375857
Note
QC 20120808. Updated from accepted to published.Available from: 2010-12-09 Created: 2010-12-09 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Urban Land-cover Mapping with High-resolution Spaceborne SAR Data
Open this publication in new window or tab >>Urban Land-cover Mapping with High-resolution Spaceborne SAR Data
2010 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Urban areas around the world are changing constantly and therefore it is necessary to update urban land cover maps regularly. Remote sensing techniques have been used to monitor changes and update land-use/land-cover information in urban areas for decades. Optical imaging systems have received most of the attention in urban studies. The development of SAR applications in urban monitoring has been accelerated with more and more advanced SAR systems operating in space.

 

This research investigated object-based and rule-based classification methodologies for extracting urban land-cover information from high resolution SAR data. The study area is located in the north and northwest part of the Greater Toronto Area (GTA), Ontario, Canada, which has been undergoing rapid urban growth during the past decades. Five-date RADARSAT-1 fine-beam C-HH SAR images with a spatial resolution of 10 meters were acquired during May to August in 2002. Three-date RADARSAT-2 ultra-fine-beam C-HH SAR images with a spatial resolution of 3 meters were acquired during June to September in 2008.

 

SAR images were pre-processed and then segmented using multi-resolution segmentation algorithm. Specific features such as geometric and texture features were selected and calculated for image objects derived from the segmentation of SAR images. Both neural network (NN) and support vector machines (SVM) were investigated for the supervised classification of image objects of RADARSAT-1 SAR images, while SVM was employed to classify image objects of RADARSAT-2 SAR images. Knowledge-based rules were developed and applied to resolve the confusion among some classes in the object-based classification results.

 

The classification of both RADARSAT-1 and RADARSAT-2 SAR images yielded relatively high accuracies (over 80%). SVM classifier generated better result than NN classifier for the object-based supervised classification of RADARSAT-1 SAR images. Well-designed knowledge-based rules could increase the accuracies of some classes after the object-based supervised classification. The comparison of the classification results of RADARSAT-1 and RADARSAT-2 SAR images showed that SAR images with higher resolution could reveal more details, but might produce lower classification accuracies for certain land cover classes due to the increasing complexity of the images. Overall, the classification results indicate that the proposed object-based and rule-based approaches have potential for operational urban land cover mapping from high-resolution space borne SAR images.

Place, publisher, year, edition, pages
Stockholm: KTH, 2010. vi, 42 p.
Series
Trita-SOM , ISSN 1653-6126
Keyword
High-resolution, RADARSAT-1 SAR, RADARSAT-2 SAR, Urban Land Cover, Object-based Classification, Neural Network, Support Vector Machines.
National Category
Agricultural Science
Identifiers
urn:nbn:se:kth:diva-26931 (URN)978-91-7415-805-2 (ISBN)
Presentation
2010-12-10, 5055, Drottning Kristinas väg 30, Stockholm, 13:00 (English)
Opponent
Supervisors
Note
QC 20101209Available from: 2010-12-09 Created: 2010-11-29 Last updated: 2010-12-09Bibliographically approved

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Citation style
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