Remote Sensing Image Segmentation Based on Improved Statistical Region Merging

Xiu Li Gong; Zhi Ming Wang

doi:10.4028/www.scientific.net/AMM.667.226

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 667Remote Sensing Image Segmentation Based on...

Remote Sensing Image Segmentation Based on Improved Statistical Region Merging

Abstract:

Statistical Region Merging (SRM) is an efficient image segmentation algorithm for images with noise and partial occlusion. However, due to the complexity of remote sensing image, SRM can’t give satisfactory results. This paper proposes an improved image segmentation algorithm for remote sensing image based on SRM. Firstly, 8-connexity gradient estimation models are used to obtain more precisely edges. Secondly, the dissimilarity criterion between regions is replaced by a normalized distance standard. Finally, it dynamically updates and sorts dissimilarity between regions during region merging. Experimental results show the proposed algorithm can achieve better segmentation results from coarse to fine compared with original SRM.

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Periodical:

Applied Mechanics and Materials (Volume 667)

Pages:

226-229

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.667.226

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Online since:

October 2014

Authors:

Xiu Li Gong, Zhi Ming Wang

Keywords:

Image Segmentation, Remote Sensing Image, Statistical Region Merging (SRM)

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