School of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen 518055, China.
Comput Intell Neurosci. 2021 Aug 4;2021:6572362. doi: 10.1155/2021/6572362. eCollection 2021.
The synthetic aperture radar (SAR) image preprocessing techniques and their impact on target recognition performance are researched. The performance of SAR target recognition is improved by composing a variety of preprocessing techniques. The preprocessing techniques achieve the effects of suppressing background redundancy and enhancing target characteristics by processing the size and gray distribution of the original SAR image, thereby improving the subsequent target recognition performance. In this study, image cropping, target segmentation, and image enhancement algorithms are used to preprocess the original SAR image, and the target recognition performance is effectively improved by combining the above three preprocessing techniques. On the basis of image enhancement, the monogenic signal is used for feature extraction and then the sparse representation-based classification (SRC) is used to complete the decision. The experiments are conveyed on the moving and stationary target acquisition and recognition (MSTAR) dataset, and the results prove that the combination of multiple preprocessing techniques can effectively improve the SAR target recognition performance.
研究了合成孔径雷达（SAR）图像预处理技术及其对目标识别性能的影响。通过组合多种预处理技术，可以提高 SAR 目标识别的性能。预处理技术通过处理原始 SAR 图像的大小和灰度分布来实现抑制背景冗余和增强目标特征的效果，从而提高后续目标识别的性能。在本研究中，使用图像裁剪、目标分割和图像增强算法对原始 SAR 图像进行预处理，并通过结合上述三种预处理技术，有效地提高了目标识别的性能。在图像增强的基础上，使用单态信号进行特征提取，然后使用基于稀疏表示的分类（SRC）完成决策。实验在运动和静止目标获取和识别（MSTAR）数据集上进行，结果证明，多种预处理技术的组合可以有效地提高 SAR 目标识别的性能。
