Remote sensing image segmentation based on self-organizing map at multiple-scale

Zhisheng Zhou; Shiyan Wei; Xuewen Zhang; Xian Zhao

doi:10.1117/12.760420

8 August 2007 Remote sensing image segmentation based on self-organizing map at multiple-scale

Zhisheng Zhou, Shiyan Wei, Xuewen Zhang, Xian Zhao

Proceedings Volume 6752, Geoinformatics 2007: Remotely Sensed Data and Information; 67520E (2007) https://doi.org/10.1117/12.760420
Event: Geoinformatics 2007, 2007, Nanjing, China

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.

Citation Download Citation

Zhisheng Zhou, Shiyan Wei, Xuewen Zhang, and Xian Zhao "Remote sensing image segmentation based on self-organizing map at multiple-scale", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67520E (8 August 2007); https://doi.org/10.1117/12.760420

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