In this letter,a multiphase level set approach unifying region and boundary-based infor-mation for multi-region segmentation of Synthetic Aperture Radar(SAR)image is presented.Anenergy functional that is applicable for SAR image segmentation is defined.It consists of two termsdescribing the local statistic characteristics and the gradient characteristics of SAR image respectively.A multiphase level set model that explicitly describes the different regions in one image is proposed.The purpose of such a multiphase model is not only to simplify the way of denoting multi-region by levelset but also to guarantee the accuracy of segmentation.According to the presented multiphase model,the curve evolution equations with respect to edge curves are deduced.The multi-region segmentationis implemented by the numeric solution of the partial differential equations.The performance of theapproach is verified by both simulation and real SAR images.The experiments show that the proposedalgorithm reduces the speckle effect on segmentation and increases the boundary alignment accuracy,thus correctly divides the multi-region SAR image into different homogenous regions. In this letter, a multiphase level set approach unifying region and boundary-based infor-mation for multi-region segmentation of Synthetic Aperture Radar (SAR) image is presented. Energy functional that is applicable for SAR image segmentation is defined. It consists of two termsdescribing the local statistic characteristics and the gradient characteristics of SAR image respectively. A multiphase level set model that explicitly describes the different regions in one image is proposed. The purpose of such a multiphase model is not only to simplify the way of denoting multi-region by levelset but also to guarantee the accuracy of segmentation. Accredited to the presented multiphase model, the curve evolution equations with respect to edge curves are deduced. The multi-region segmentation is implemented by the numeric solution of the partial differential equations. The performance of theapproach is verified by both simulation and real SAR images. These experiments show that the proposed algorithm reduces s the speckle effect on segmentation and increases the boundary alignment accuracy, thus correctly divides the multi-region SAR image into different homogenous regions.