Abstract
Fuzzy c-means (FCM) algorithm is a popular method in image segmentation and image classification. However, the traditional FCM algorithm cannot achieve satisfactory classification results because remote sensing image data are not subjected to Gaussian distribution, contain some types of noise, are nonlinear, and lack labeled data. This paper presents a robust semi-supervised kernel-FCM algorithm incorporating local spatial information (RSSKFCM_S) to solve the aforementioned problems. In the proposed algorithm, insensitivity to noise is enhanced by introducing contextual spatial information. The non-Euclidean structure and the problem in nonlinearity are resolved through kernel methods. Semi-supervised learning technique is utilized to supervise the iterative process to reduce step number and improve classification accuracy. Finally, the performance of the proposed RSSKFCM_S algorithm is tested and compared with several similar approaches. Experimental results for the multispectral remote sensing image show that the RSSKFCM_S algorithm is more effective and efficient.
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Acknowledgments
The authors would like to thank the anonymous referees for their helpful comments and suggestions to improve the presentation of the paper. Special thanks should be given to Dr. Banglong Pan and his colleagues, for providing the remote sensing image data for validation of our algorithm. This research was supported by Anhui Provincial Natural Science Foundation under Grant 1308085QD70.
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Zhu, C., Yang, S., Zhao, Q. et al. Robust Semi-supervised Kernel-FCM Algorithm Incorporating Local Spatial Information for Remote Sensing Image Classification. J Indian Soc Remote Sens 42, 35–49 (2014). https://doi.org/10.1007/s12524-013-0296-x
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DOI: https://doi.org/10.1007/s12524-013-0296-x