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Journal of Applied Remote Sensing

Unsupervised spectropolarimetric imagery clustering fusion
Author(s): Yongqiang Zhao; Peng Gong; Quan Pan
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Paper 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.

Paper Details

Date Published: 1 June 2009
PDF: 16 pages
J. Appl. Remote Sens. 3(1) 033535 doi: 10.1117/1.3168619
Published in: Journal of Applied Remote Sensing Volume 3, Issue 1
Show Author Affiliations
Yongqiang Zhao, Northwestern Polytechnical Univ. (China)
Peng Gong, Univ. of California, Berkeley (United States)
Quan Pan, Northwestern Polytechnical Univ. (China)


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