Information cut in remote sensing image segmentation
遥感图像分割中的信息割算法

A modified information cut algorithm(MIC) is presented. First, information cut(IC) model is demonstrated to be equivalent to Cauchy-Schwarz cut(CScut), and then the optimal solution of IC objective function using graph spectral method is proposed; Using both the gray and space relationship of pixels in an image, a MIC algorithm is proposed based on IC algorithm, this method firstly utilizes Parzen windowing function that combines gray information and space information to evaluate probability density functions, and thus reduces the effect of gray changes to image segmentation. Experiments using synthetic image with noise and remote sensing images indicate that MIC algorithm has better anti-noise performance than IC algorithm, and lower computational complexity compared with graph spectral methods.