High-Resolution Remote Multi-Spectral Sensing Images Based on Texture Features

Abstract:

Aiming at the difficulties in the segmentation for high-resolution remote multispectral sensing images, this paper proposed a segmentation approach for remote sensing images based on texture features. The algorithm implemented precipitation watershed transform respectively on the texture images obtained by the different characteristics of GLCM, and then superimposed the two segmentation results, finally completing the image segmentation by using a novel regional consolidation method that combined the texture features. The experiments were implemented on the high-resolution ALOS and SPOT 5 remote sensing images respectively. Compared with the traditional watershed segmentation approach based on gradient information, the experimental results showed that the proposed algorithm can accurately locate the edges of objects, effectively overcome the phenomenon of over-segmentation and under-segmentation, with a higher segmentation accuracy and stability.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 3596-3599
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.3596
• Online since: November 2014
• Authors: Chen Ming Li*, Li Qin Zhu, Qiang Wang, Zhen Sun, Feng Chen Huang, Chen He
• Keywords: Image Segmentation, Region Merging, Resolution Remote Sensing Image, Texture Feature, Watershed Transformation

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DOI: 10.1109/iscas.1999.779933