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Proceedings Paper

A self-adaptive mean-shift segmentation approach based on graph theory for high-resolution remote sensing images
Author(s): Luwan Chen; Ling Han; Xiaohong Ning
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Paper Abstract

An auto new segmentation approach based on graph theory which named self-adaptive mean-shift for high-resolution remote sensing images was proposed in this paper. This approach could overcome some defects that classic Mean-Shift must determine the fixed bandwidth through trial many times, and could effectively distinguish the difference between different features in the texture rich region. Segmentation experiments were processed with WorldView satellite image. The results show that the presented method is adaptive, and its speed and precision can satisfy application, so it is a robust automatic segmentation algorithm.

Paper Details

Date Published: 9 December 2015
PDF: 8 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98081X (9 December 2015); doi: 10.1117/12.2208795
Show Author Affiliations
Luwan Chen, Chang'an Univ. (China)
Ling Han, Chang'an Univ. (China)
Xiaohong Ning, Chang'an Univ. (China)


Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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