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Proceedings Paper

Remote sensing image segmentation based on self-organizing map at multiple-scale
Author(s): Zhisheng Zhou; Shiyan Wei; Xuewen Zhang; Xian Zhao
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Paper Abstract

This paper proposes a segmentation method based on K-mean and SOM network. Firstly remote sensing image is decomposed by wavelet transform at multiple-scale. Secondly the directional eigenvector of the image is constructed based on the wavelet transform. At coarser scale, we construct 4-dimension eigenvector with feature images, and the images are roughly segmented by K-means algorithm. Then we construct 4-dimension eigenvector with other feature images at fine scale. Based on the results in K-means segmentation and the eigenvector of remote-sensing images at fine scale the images are segmented by SOM network. The experiments about the images segmentation are done in two different ways, one of which is K-means and SOM network simultaneously, and the other of which is mere K-mean. The experiments show that the former has better segmentation results and higher efficiency.

Paper Details

Date Published: 8 August 2007
PDF: 9 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67520E (8 August 2007); doi: 10.1117/12.760420
Show Author Affiliations
Zhisheng Zhou, Beijing Institute of Civil Engineering and Architecture (China)
Shiyan Wei, Beijing Institute of Civil Engineering and Architecture (China)
Xuewen Zhang, Beijing Institute of Civil Engineering and Architecture (China)
Xian Zhao, Beijing Institute of Civil Engineering and Architecture (China)


Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information

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