Image Segmentation Based on Rough Entropy and K-Means Clustering Algorithm

An image segmentation algorithm is based on "rough entropy" and its computational complexity is linear.But as a method of single threshold segmentation,it can not satisfy the need for multi-class segmentation of complex image.Therefore,K-means clustering algorithm is used to segment the(image) based on region firstly,And then the algorithm based on "rough entropy" is used to extract objects from the segmentation results,so as to achieve multi-threshold segmentation.The experimental results of remote sensing image segmentation demonstrate that the improved approach is valid.