8 March 2018 Unsupervised classification of high-resolution remote-sensing images under edge constraints
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091C (2018) https://doi.org/10.1117/12.2285777
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
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.
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Wenying Ge, Wenying Ge, Guoying Liu, Guoying Liu, } "Unsupervised classification of high-resolution remote-sensing images under edge constraints", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091C (8 March 2018); doi: 10.1117/12.2285777; https://doi.org/10.1117/12.2285777
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