目的 目标轮廓表征了目标形状，可用于目标方位角估计、自动目标识别等，因此提取合成孔径雷达（SAR）图像中的目标轮廓受到了人们的广泛关注。受SAR图像乘性噪声的影响，传统的目标轮廓提取方法应用在SAR图像时失效。针对这一问题，提出一种将基于边缘的活动轮廓模型和基于区域的活动轮廓模型相结合的活动轮廓模型。方法 以真实SAR图像为基础，分析了向量场卷积（VFC）活动轮廓模型以及区域竞争（RC）活动轮廓模型各自的特点和优势，发现这两个模型存在一定的互补性，因此将这两个模型进行了结合，得到了一种新的SAR图像目标轮廓提取方法。结果 基于真实SAR图像的实验结果表明，本文方法能较好地应对SAR图像信噪比较低、目标边缘模糊等特点，能准确地获得SAR图像目标轮廓。结论 本文方法可用于执行实际的SAR图像轮廓提取任务，为后续的SAR图像自动识别和特征级图像融合等任务提供了较为优良的输入信息。

Objective 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. Method 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. Result 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. Conclusion 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.