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Proceedings Paper

Clustering of high-resolution remote sensing imagery
Author(s): Xiangjin Deng; Yanping Wang; Risheng Yun; Hailiang Peng
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Paper Abstract

The development of the remotely sensed techniques enlarges the applications of the remote sensing imagery. The clustering of high resolution imagery is difficult, due to the fact that the minor objects, such as roads, make the appearance of the same category region non-uniform. This paper proposes a new approach to cluster high resolution remote sensing imagery. The new clustering approach includes three steps as the following: Firstly, eliminate the minor components in the moving windows. Secondly, compute the image features, such as the energy, some high order cumulants and central moments of pixels' values in moving windows. Lastly, apply the BPC neural network, which is combined by a Back-Propagation (BP) neural network and a Competive neural network, to cluster images according to the image features. Two methods, minimum distance method and the K-means method, are compared with the new clustering approach, proposed by this paper, by using SPOT images for clustering residential areas and agricultural areas in the suburbs of Beijing. The experimental results show that the new clustering approach has the higher clustering accuracy.

Paper Details

Date Published: 11 June 2003
PDF: 8 pages
Proc. SPIE 4898, Image Processing and Pattern Recognition in Remote Sensing, (11 June 2003); doi: 10.1117/12.467315
Show Author Affiliations
Xiangjin Deng, Institute of Electronics (China)
Yanping Wang, Institute of Electronics (China)
Risheng Yun, Institute of Electronics (China)
Hailiang Peng, Institute of Electronics (China)

Published in SPIE Proceedings Vol. 4898:
Image Processing and Pattern Recognition in Remote Sensing
Stephen G. Ungar; Shiyi Mao; Yoshifumi Yasuoka, Editor(s)

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