目标识别是SAR图像解译的关键环节, 针对已有基于稀疏表示的SAR图像目标识别方法识别率不高的问题, 在分析影响识别率原因的基础上, 结合SAR图像中目标区域和阴影区域特性, 提出了一种基于稀疏表示和拉伸变换的SAR图像目标识别方法。该方法通过对训练样本图像进行拉伸变换生成了新的训练样本图像, 利用已有的和新的训练样本图像构造稀疏字典, 通过求解目标区域和阴影区域的联合稀疏表示, 根据重构误差最小准则完成了SAR图像目标识别。利用MSTAR实测SAR图像对提出的目标识别方法进行了测试, 结果表明新方法识别率高于已有方法, 从而验证了新方法的有效性。

Target recognition is the key link of SAR image interpretation. The recognition rate of the existing SAR image target recognition method using sparse representation is not high enough. Therefore, we proposed a SAR image target recognition method using sparse representation and stretch transformation based on the analysis of the factors affecting the recognition rate, and according to the characteristics of the target region and the shadow area. This method may generate a new training sample image by stretching the training sample image, and construct a sparse dictionary by using the existing and new training sample image. By solving the joint sparse representation of the target region and the shadow area, the SAR image target recognition is completed according to the criterion of the minimum reconstruction error. The proposed method of target recognition was tested by using MSTAR SAR image. The results show that the recognition rate of the proposed method is higher than that of the existing method, and thus the validity of the method is verified.