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Journal of Applied Remote Sensing

Improved superpixel-based polarimetric synthetic aperture radar image classification integrating color features
Author(s): Yanxiao Xing; Yi Zhang; Ning Li; Robert Wang; Guixiang Hu
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Paper Abstract

Various polarimetric features including scattering matrix, covariance matrix, polarimetric decomposition results, and textural or spatial information have already been used for polarimetric synthetic aperture radar (PolSAR) image classification. However, color features are rarely involved. We propose an improved superpixel-based PolSAR image classification integrating color features. First, we extract the color information using polarimetric decomposition. Second, by combining the color and spatial information of pixels, modified simple linear iterative clustering is used to generate small regions called superpixels. Then we apply Wishart distance to the superpixels to classify them into different classes. This method is demonstrated using the L-band Flevoland PolSAR data from AirSAR and Oberpfaffenhofen PolSAR data from ESAR. The results show that this method works well for areas with homogeneous terrains like farms in terms of both classification accuracy and computational efficiency. Furthermore, the success of the proposed method signifies that more color features can be discovered in the future research works.

Paper Details

Date Published: 27 May 2016
PDF: 15 pages
J. Appl. Rem. Sens. 10(2) 026026 doi: 10.1117/1.JRS.10.026026
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Yanxiao Xing, Institute of Electronics (China)
Univ. of the Chinese Academy of Sciences (China)
Yi Zhang, Institute of Electronics (China)
Ning Li, Institute of Electronics (China)
Robert Wang, Institute of Electronics (China)
Guixiang Hu, Institute of Electronics (China)
Univ. of the Chinese Academy of Sciences (China)

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