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.