A self-adaptive mean-shift segmentation approach based on graph theory for high-resolution remote sensing images

Luwan Chen; Ling Han; Xiaohong Ning

doi:10.1117/12.2208795

9 December 2015 A self-adaptive mean-shift segmentation approach based on graph theory for high-resolution remote sensing images

Luwan Chen, Ling Han, Xiaohong Ning

Author Affiliations +

Proceedings Volume 9808, International Conference on Intelligent Earth Observing and Applications 2015; 98081X (2015) https://doi.org/10.1117/12.2208795
Event: International Conference on Intelligent Earth Observing and Applications, 2015, Guilin, China

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.

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Luwan Chen, Ling Han, and Xiaohong Ning "A self-adaptive mean-shift segmentation approach based on graph theory for high-resolution remote sensing images", Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98081X (9 December 2015); https://doi.org/10.1117/12.2208795

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KEYWORDS

Image segmentation

Remote sensing

Image processing algorithms and systems

Statistical analysis

Image processing

Detection and tracking algorithms

Feature extraction

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