Target contours provide valuable shape information that can be used in pose estimation and target recognition. Therefore

target contour extraction in synthetic aperture radar (SAR) image has drawn much attention around the world. Given the multiplicative noise inherent in SAR image

traditional target contour extraction methods fail to process SAR images. To solve this problem

this paper proposes a new active contour model that combines the attributes of both edges and regions. Based on real SAR images

this paper analyzes the vector field convolution active contour model and the region competition active contour model. The two active contour models have their own advantages and complement each other. Therefore

we combine the two active contour models to obtain a new active contour model that can be used for target contour extraction in SAR image. The experiments are conducted based on real SAR images

and results show that the proposed method can deal with the characteristics of SAR image

such as low signal-to-noise ratio and blurred target edges. It can accurately locate the contours of the targets in SAR image. The proposed method can be applied to SAR image interpretation to extract target contours to provide excellent input information for subsequent tasks in SAR image interpretation

such as automatic identification and feature-level image fusion.