中文摘要：

地面目标的SAR图像中除了包含目标散射回波形成的区域，还包括由目标遮挡地面形成的阴影区域。但是由于这两种区域中的图像特性不相同，所以传统的SAR图像自动目标识别主要利用目标区域信息进行目标识别，或者单独使用阴影区域进行识别。该文提出一种阴影区域与目标区域图像联合的稀疏表示模型。通过使用l1／l2范数最小化方法求解该模型得到联合的稀疏表示，然后根据联合重构误差最小准则进行SAR图像目标识别。在运动和静止目标获取与识别（MSTAR）数据集上的识别实验结果表明，通过联合稀疏表示模型可以有效地将目标区域与阴影区域信息进行融合，相对于采用单独区域图像的稀疏表示识别方法性能更好。

英文摘要：

SAR image of the ground target contains the target region formed by the scattered echoes of the target as well as the shadow area. However, the characteristics of the two areas are essentially different, therefore the traditional SAR image Automatic Target Recognition （ATR） methods use mainly target area information alone or shadow region only for recognition. This paper presents a joint sparse representation model by combining images of the shadow region and target region. By using thel1／l2norm minimization method to solve the joint sparse representation model, the SAR image target recognition is achieved by minimizing the joint reconstruction error. Recognition results on Moving and Stationary Target Acquisition and Recognition （MSTAR） data sets show that the joint sparse representation model can effectively fuse the information within the target region and shadow region, and it has much better recognition performance than the methods using only the target or shadow area information of the image.