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Proceedings Paper

Unsupervised classification of high-resolution remote-sensing images under edge constraints
Author(s): Wenying Ge; Guoying Liu
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Paper Abstract

Classification is a crucial task in various remote sensing applications. While edge is one of the most important characteristics in the high-resolution remote-sensing images, which helps much for the improvement of classification accuracy. Therefore, in this paper, we propose an unsupervised classification method by incorporating edge information into a clustering procedure. Firstly, a consistency coefficient function, which indicates the similarity between edges obtained by clustering and by the edge detection methods, is defined to guarantee more accurate edges. Sequentially, a clustering procedure based on HMRFFCM is designed, in which the edge constraints are exploited by using the edge consistency. Experiments on synthetic and real remote sensing images have shown that the proposed methods can get more accurate classification results.

Paper Details

Date Published: 8 March 2018
PDF: 6 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091C (8 March 2018); doi: 10.1117/12.2285777
Show Author Affiliations
Wenying Ge, Anyang Normal Univ. (China)
Guoying Liu, Anyang Normal Univ. (China)

Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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