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Journal of Applied Remote Sensing

Object-oriented fusion of RADARSAT-2 polarimetric synthetic aperture radar and HJ-1A multispectral data for land-cover classification
Author(s): Yan Xiao; Qigang Jiang; Bin Wang; Yuanhua Li; Shu Liu; Can Cui
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Paper Abstract

The contribution of the integration of optical and polarimetric synthetic aperture radar (PolSAR) data to accurate land-cover classification was investigated. For this purpose, an object-oriented classification methodology that consisted of polarimetric decomposition, hybrid feature selection, and a support vector machine (SVM) was proposed. A RADARSAT-2 Fine Quad-Pol image and an HJ-1A CCD2 multispectral image were used as data sources. First, polarimetric decomposition was implemented for the RADARSAT-2 image. Sixty-one polarimetric parameters were extracted using different polarimetric decomposition methods and then merged with the main diagonal elements (T11, T22, T33) of the coherency matrix to form a multichannel image with 64 layers. Second, the HJ-1A and the multichannel images were divided into numerous image objects by implementing multiresolution segmentation. Third, 1104 features were extracted from the HJ-1A and the multichannel images for each image object. Fourth, the hybrid feature selection method that combined the ReliefF filter approach and the genetic algorithm (GA) wrapper approach (ReliefF–GA) was used. Finally, land-cover classification was performed by an SVM classifier on the basis of the selected features. Five other classification methodologies were conducted for comparison to verify the contribution of optical and PolSAR data integration and to test the superiority of the proposed object-oriented classification methodology. Comparison results show that HJ-1A data, RADARSAT-2 data, polarimetric decomposition, ReliefF–GA, and SVM have a significant contribution by improving land-cover classification accuracy.

Paper Details

Date Published: 18 May 2016
PDF: 17 pages
J. Appl. Rem. Sens. 10(2) 026021 doi: 10.1117/1.JRS.10.026021
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Yan Xiao, Jilin Univ. (China)
Qigang Jiang, Jilin Univ. (China)
Bin Wang, Changchun Institute of Surveying and Mapping (China)
Yuanhua Li, Jilin Univ. (China)
Shu Liu, Jilin Univ. (China)
Can Cui, Dalian Maritime Univ. (China)

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