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Image change detection based on an improved rough fuzzy c-means clustering algorithm

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Abstract

An unsupervised change detection method based on an improved rough fuzzy c-means clustering method (SRFPCM) for synthetic aperture radar and optical remote sensing images is proposed. SRFPCM incorporates the local spatial information and gray level information in a novel fuzzy way, aiming at guaranteeing noise insensitiveness and image detail preservation. Inspired by the idea of a robust fuzzy local information c-means clustering algorithm, this new algorithm can overcome the disadvantages of rough fuzzy c-means clustering algorithm and enhance the clustering performance at the same time. SRFPCM is employed to cluster the difference image into two clusters (changed and unchanged regions) and get the change map. Experimental results confirm the effectiveness of the proposed algorithm.

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Acknowledgments

This work was supported by the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (Grant No. B07048), the National Natural Science Foundation of China (Grant Nos. 61203303, 61202176, 61273317, 61272279).

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Authors and Affiliations

  1. Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, No. 2 South Taibai Road, Shaanxi, 710071, Xi’an, China

    Wenping Ma, Licheng Jiao, Maoguo Gong & Congling Li

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  1. Wenping Ma
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  2. Licheng Jiao
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  3. Maoguo Gong
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  4. Congling Li
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Correspondence to Wenping Ma.

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Ma, W., Jiao, L., Gong, M. et al. Image change detection based on an improved rough fuzzy c-means clustering algorithm. Int. J. Mach. Learn. & Cyber. 5, 369–377 (2014). https://doi.org/10.1007/s13042-013-0174-4

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  • DOI: https://doi.org/10.1007/s13042-013-0174-4

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