Research Papers

Unsupervised spectropolarimetric imagery clustering fusion

[+] Author Affiliations
Yongqiang Zhao

The College of Automation, Northwestern Polytechnical University, 127 Youyi Xilu, Xi An, Shanxi 710072 China

Peng Gong

State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Chinese Academy of Science, Beijing, China 100101

Quan Pan

The College of Automation, Northwestern Polytechnical University, 127 Youyi Xilu, Xi An, Shanxi 710072 China

J. Appl. Remote Sens. 3(1), 033535 (June 15, 2009). doi:10.1117/1.3168619
History: Received October 18, 2007; Revised June 7, 2009; Accepted June 11, 2009; June 15, 2009; Online June 15, 2009
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Abstract

In the past few years, imaging spectroscopy has been used widely. However, it only acquires intensity information in a narrow electromagnetic band, ignoring the polarimetric information of the electromagnetic wave, resulting in inaccurate material classification. Imaging spectropolarimetric technology as a new sensing method can acquire the polarimetric information at a narrow electromagnetic band sequence, but there are few results showing how to combine the redundant and complementary features provided by spectropolarimetric imagery. In this paper, an unsupervised spectropolarimetric imagery classification method is proposed to jointly utilize the spatial, spectral and polarimetric information to make material classification more accurate. First, a spectropolarimetric projection scheme is proposed to divide the spectropolarimetric data set into two parts: a polarimetric spectrum data set and a polarimetric data cube. Then, a kernel fuzzy c-means clustering method is used to cluster the polarimetric spectrum data set and polarimetric data cubes. At last, kernel fuzzy c-means clustering results are combined by evidence reasoning to get better clustering performance. Through experimentation and simulation, the effects of classifying different materials with similar surface colour can be enhanced greatly.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Yongqiang Zhao ; Peng Gong and Quan Pan
"Unsupervised spectropolarimetric imagery clustering fusion", J. Appl. Remote Sens. 3(1), 033535 (June 15, 2009). ; http://dx.doi.org/10.1117/1.3168619


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