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Multitemporal RADARSAT-2 polarimetric SAR data for urban cover classification using support vector machine
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi. (Geoinformatics)
2010 (English)In: 30th EARSeL Symposium, Paris, France, June, 2010, 2010, 581-588 p.Conference 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. 




Place, publisher, year, edition, pages
2010. 581-588 p.
Keyword [en]
Polarimetric SAR, multitemporal, landuse/land-cover, SVM, object-based analysis
National Category
Remote Sensing
URN: urn:nbn:se:kth:diva-31426ISBN: 978-3-00-033435-1OAI: diva2:403802
30th EARSeL Symposium, Paris, France, June, 2010
QC 20110315Available from: 2011-03-15 Created: 2011-03-15 Last updated: 2012-03-07Bibliographically approved
In thesis
1. Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping
Open this publication in new window or tab >>Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping
2011 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Urban represents one of the most dynamic areas in the global change context. To support rational policies for sustainable urban development, remote sensing technologies such as Synthetic Aperture Radar (SAR) enjoy increasing popularity for collecting up-to-date and reliable information such as urban land cover/land-use. With the launch of advanced spaceborne SAR sensors such as RADARSAT-2, multitemporal fully polarimetric SAR data in high-resolution become increasingly available. Therefore, development of new methodologies to analyze such data for detailed and accurate urban mapping is in demand.


This research investigated multitemporal fine resolution spaceborne polarimetric SAR (PolSAR) data for detailed urban land cover mapping. To this end, the north and northwest parts of the Greater Toronto Area (GTA), Ontario, Canada were selected as the study area. Six-date C-band RADARSAT-2 fine-beam full polarimetric SAR data were acquired during June to September in 2008. Detailed urban land covers and various natural classes were focused in this study.


Both object-based and pixel-based classification schemes were investigated for detailed urban land cover mapping. For the object-based approaches, Support Vector Machine (SVM) and rule-based classification method were combined to evaluate the classification capacities of various polarimetric features. Classification efficiencies of various multitemporal data combination forms were assessed. For the pixel-based approach, a temporal-spatial Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) analysis and multitemporal mixture models, contextual information was explored in the classification process. Moreover, the fitness of alternative data distribution assumptions of multi-look PolSAR data were compared for detailed urban mapping by this algorithm.


Both the object-based and pixel-based classifications could produce the finer urban structures with high accuracy. The superiority of SVM was demonstrated by comparison with the Nearest Neighbor (NN) classifier in object-based cases. Efficient polarimetric parameters such as Pauli parameters and processing approaches such as logarithmically scaling of the data were found to be useful to improve the classification results. Combination of both the ascending and descending data with appropriate temporal span are suitable for urban land cover mapping. The SEM algorithm could preserve the detailed urban features with high classification accuracy while simultaneously overcoming the speckles. Additionally the fitness of the G0p and Kp distribution assumptions were demonstrated better than the Wishart one.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2011. viii, 54 p.
Trita-SOM , ISSN 1653-6126 ; 2011-05
RADARSAT-2, spaceborne, polarimetric SAR, urban land cover, classification
National Category
Computer and Information Science
urn:nbn:se:kth:diva-31176 (URN)978-91-7415-909-7 (ISBN)
2011-03-16, Seminarierum 4055, KTH, Drottning Kristinas väg 30, Stockholm, 14:17 (English)

QC 20110315

Available from: 2011-03-15 Created: 2011-03-10 Last updated: 2013-12-04Bibliographically approved

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