Improved superpixel-based polarimetric synthetic aperture radar image classification integrating color features

Yanxiao Xing; Yi Zhang; Ning Li; Robert Wang; Guixiang Hu

doi:10.1117/1.JRS.10.026026

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.

Citation Download Citation

Yanxiao Xing, Yi Zhang, Ning Li, Robert Wang, Guixiang Hu, "Improved superpixel-based polarimetric synthetic aperture radar image classification integrating color features," Journal of Applied Remote Sensing 10(2), 026026 (27 May 2016). https://doi.org/10.1117/1.JRS.10.026026 . Submission:

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