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Proceedings Paper

Classification of PolSAR image based on quotient space theory
Author(s): Zhihui An; Jie Yu; Xiaomeng Liu; Limin Liu; Shuai Jiao; Teng Zhu; Shaohua Wang
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Paper Abstract

In order to improve the classification accuracy, quotient space theory was applied in the classification of polarimetric SAR (PolSAR) image. Firstly, Yamaguchi decomposition method is adopted, which can get the polarimetric characteristic of the image. At the same time, Gray level Co-occurrence Matrix (GLCM) and Gabor wavelet are used to get texture feature, respectively. Secondly, combined with texture feature and polarimetric characteristic, Support Vector Machine (SVM) classifier is used for initial classification to establish different granularity spaces. Finally, according to the quotient space granularity synthetic theory, we merge and reason the different quotient spaces to get the comprehensive classification result. Method proposed in this paper is tested with L-band AIRSAR of San Francisco bay. The result shows that the comprehensive classification result based on the theory of quotient space is superior to the classification result of single granularity space.

Paper Details

Date Published: 17 December 2015
PDF: 6 pages
Proc. SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 98110R (17 December 2015); doi: 10.1117/12.2205729
Show Author Affiliations
Zhihui An, Capital Normal Univ. (China)
Jie Yu, Capital Normal Univ. (China)
Xiaomeng Liu, Capital Normal Univ. (China)
Limin Liu, Wuhan Univ. (China)
Shuai Jiao, Capital Normal Univ. (China)
Teng Zhu, Capital Normal Univ. (China)
Shaohua Wang, Capital Normal Univ. (China)


Published in SPIE Proceedings Vol. 9811:
MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis
Jinxue Wang; Zhiguo Cao; Jayaram K. Udupa; Henri Maître, Editor(s)

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