Multispectral remote sensing image segmentation using rival penalized controlled competitive learning and fuzzy entropy

• DOI: 10.1007/s00500-015-1601-0
• Publication Date: 2015-02-04T07:08:15Z
• Authors: Huan Xie; Xin Luo; Chao Wang; Shijie Liu; Xiong Xu; Xiaohua Tong
• Keywords: Multispectral pattern recognition; Segmentation-based object categorization; Region growing

This paper proposes an image segmentation approach for multispectral remote sensing imagery based on rival penalized controlled competitive learning (RPCCL) and fuzzy entropy. In this approach, the clustering center component for each band of the image is first chosen based on the fuzzy entropy histogram of the corresponding band of the image. The initial clustering centers are then formed by combining the obtained clustering center components. The number of clusters and the real clustering centers are then determined by the use of the RPCCL method. The advantages of the proposed approach are the appropriate initial cluster centers and the fact that the number of clusters is determined automatically. The results of the experiments showed that without providing the number of clustering centers before the clustering operation, the proposed method can effectively perform an unsupervised segmentation of remote sensing images.