SAR图像自动目标识别(automatic target recognition,ATR)是当前的一大研究热点。典型的ATR系统分为检测、辨别和识别3个阶段。在检测和辨别过程中对图像进行预筛选,找出感兴趣区域,是进行目标识别前的一个重要步骤。高效的预筛选过程可以大大减少目标识别过程的计算量。目前,目标检测的方法有CFAR方法、多分辨率方法以及基于相位信息的检测方法3类。目标辨别的方法也有多种。本文就目标检测和辨别阶段的主要算法及其效果作了系统的介绍,并对该领域未来的发展方向进行了展望。

Automatic Target Recognition in SAR imagery becomes popular in recent years. The typical Automatic Target Recognition system consists of three stages: detection, discrimination and classification. Detection, whose role is to find regions in SAR imagery that contains potential targets, will inevitably produce false alarms. The false alarms are then further reduced by the following stage, discrimination. These two stages together are called prescreening, which are very important in the whole ATR system. If they act highly efficient, i.e., they can reject almost all the background clutter, the computational cost in the process of classification will be greatly reduced. At present, there are three methods in the field of automatic target detection: CFAR, multi-resolution model, detection methods based on image phases. There are also quite a few methods for target discrimination. In this paper we present an overview on the algorithms and their results for automatic target detection and discrimination in SAR imagery. The research trends of these fields are also given at the end of the paper.