The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-7/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W1, 145–148, 2013
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W1, 145–148, 2013

  12 Jul 2013

12 Jul 2013


J. Xu2,1, Z. Li1, B. Tian1, Q. Chen1, and P. Zhang1 J. Xu et al.
  • 1Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
  • 2University of Chinese Academy of Sciences, Beijing, 100049,China

Keywords: Polarimetric SAR, Classification, subspace, features

Abstract. Land cover classification is one of the most significant applications in remote sensing. Compared to optical sensing technologies, synthetic aperture radar (SAR) can penetrate through clouds and have all-weather capabilities. Therefore, land cover classification for SAR image is important in remote sensing. The subspace method is a novel method for the SAR data, which reduces data dimensionality by incorporating feature extraction into the classification process. This paper uses the averaged learning subspace method (ALSM) method that can be applied to the fully polarimetric SAR image for classification. The ALSM algorithm integrates three-component decomposition, eigenvalue/eigenvector decomposition and textural features derived from the gray-level cooccurrence matrix (GLCM). The study site, locates in the Dingxing county, in Hebei Province, China. We compare the subspace method with the traditional supervised Wishart classification. By conducting experiments on the fully polarimetric Radarsat-2 image, we conclude the proposed method yield higher classification accuracy. Therefore, the ALSM classification method is a feasible and alternative method for SAR image.