The application of gamma mixture model on Synthetic Aperture Radar（ SAR） image segmentation ignores spatial correlation between pixels. To solve this problem

this study proposes a new algorithm using regional gamma mixture model

which i mproves segmentation accuracy and reduces speckle noise effects on SAR images. The special imaging mechanism of SAR images results in severe distractions to speckle noise. The traditional gamma mixture model for SAR image segmentation only uses the gray information of images and ignores the spatial correlation between pixels

leading to segmentation result discontinuity and false segmentation. Watershed segmentation is a common initial segmentation algorithm. The defect of this algorithm is the over-segmentation phenomenon

which becomes more serious as the noise increases. Segmentation results cannot accurately represent the ground object features. Therefore

related algorithms should be used to combine segmentation results. A substantial amount of research has been conducted to solve the above problems. This study proposes a new segmentation algorithm that increases both segmentation accuracy and noise immunity. This algorithm involves two main steps: building of a regional gamma mixture model and designing of a neighborhood factor. SAR images are segmented by the watershed algorithm. The over-segmented results are used as initial clustering objects for the gamma mixture model. However

the watershed algorithm has a serious problem of over-segmentation. The watershed algorithm and gamma mixture model are combined to include regional information into calculation; however

inter-regional linkages remain missing

and segmentation accuracy remains poor. Therefore

considering the correlation between regional blocks in the parameter estimation

we include the neighborhood factor into the expectation-maximization iterative algorithm.The neighborhood factor is the weighted probability of each region belongs to each segmentation class. After several iterations

the factor is updated

and the image is segmented. Synthetic images and real SAR images demonstrate that the new algorithm can achieve more accurate segmentation results than the traditional gamma mixture model

regional Gaussian m ixture model and regional Markov random field algorithm. Upon visual observation

the segmentation results of the new algorithm are clearer and less affected by noise. Moreover

the results of the new algorithm have larger Kappa coefficient and better segmentation accuracy. This study proposes the RGaMM algorithm

which includes regional information and interregional contextual information into calculation. This algorithm can also effectively segment SAR image. In particular

under the effects of heavy speckle noise

the RGaMM algorithm has more advantages than the traditional algorithm. The proposed algorithm not only can a chieve accurate segmentation on SAR images

but also reduce speckle noise effects.