Centre of Nautical and Aviation Medicine of the PLA, Navy General Hospital, Beijing 100048, China.
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Comput Intell Neurosci. 2018 Dec 3;2018:9680465. doi: 10.1155/2018/9680465. eCollection 2018.
A synthetic aperture radar (SAR) target recognition method is proposed in this study based on the dominant scattering area (DSA). DSA is a binary image recording the positions of the dominant scattering centers in the original SAR image. It can reflect the distribution of the scattering centers as well as the preliminary shape of the target, thus providing discriminative information for SAR target recognition. By subtracting the DSA of the test image with those of its corresponding templates from different classes, the DSA residues represent the differences between the test image and various classes. To further enhance the differences, the DSA residues are subject to the binary morphological filtering, i.e., the opening operation. Afterwards, a similarity measure is defined based on the filtered DSA residues after the binary opening operation. Considering the possible variations of the constructed DSA, several different structuring elements are used during the binary morphological filtering. And a score-level fusion is performed afterwards to obtain a robust similarity. By comparing the similarities between the test image and various template classes, the target label is determined to be the one with the maximum similarity. To validate the effectiveness and robustness of the proposed method, experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and compared with several state-of-the-art SAR target recognition methods.
提出了一种基于主散射区（DSA）的合成孔径雷达（SAR）目标识别方法。DSA 是记录原始 SAR 图像中主要散射中心位置的二进制图像。它可以反映散射中心的分布以及目标的初步形状，从而为 SAR 目标识别提供有区别的信息。通过从不同类别中减去测试图像的 DSA 与其相应模板的 DSA，可以得到 DSA 残差，它表示测试图像与各种类别的差异。为了进一步增强差异，对 DSA 残差进行二进制形态滤波，即开运算。之后，基于二进制开运算后的滤波 DSA 残差定义了一个相似性度量。考虑到构建的 DSA 可能存在的变化，在二进制形态滤波过程中使用了几种不同的结构元素。然后进行评分级融合以获得稳健的相似性。通过比较测试图像与各种模板类别的相似性，确定目标标签为具有最大相似性的类别。为了验证所提出方法的有效性和鲁棒性，在运动和静止目标获取和识别（MSTAR）数据集上进行了实验，并与几种最先进的 SAR 目标识别方法进行了比较。
