Self-adaptive evolutionary algorithm for multispectral remote sensing image clustering

In this paper, a self-adaptive evolutionary clustering algorithm is presented. This algorithm uses the evolutionary programming (EP) to search the optimal clustering and bases on the principles of the K-means algorithm. The proposed self-adaptive evolutionary (SAEP) clustering algorithm self-adapts the vector of the step size appropriate for each parent. This is different from other genetic-based algorithms. The algorithm can minimize the degeneracy in the evolutionary process. The experimental results show that the KSAE clustering algorithm is efficient in the unsupervised classification of the multispectral remote sensing image.