School of Computer and Software, Nanyang Institute of Technology, Nanyang 473000, China.
College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China.
Comput Intell Neurosci. 2021 Nov 24;2021:9703709. doi: 10.1155/2021/9703709. eCollection 2021.
This paper proposes a synthetic aperture radar (SAR) image target recognition method using multiple views and inner correlation analysis. Due to the azimuth sensitivity of SAR images, the inner correlation between multiview images participating in recognition is not stable enough. To this end, the proposed method first clusters multiview SAR images based on image correlation and nonlinear correlation information entropy (NCIE) in order to obtain multiple view sets with strong internal correlations. For each view set, the multitask sparse representation is used to reconstruct the SAR images in it to obtain high-precision reconstructions. Finally, the linear weighting method is used to fuse the reconstruction errors from different view sets and the target category is determined according to the fusion error. In the experiment, the tests are conducted based on the MSTAR dataset, and the results validate the effectiveness of the proposed method.
本文提出了一种基于多视角和内部相关分析的合成孔径雷达（SAR）图像目标识别方法。由于 SAR 图像的方位敏感性，参与识别的多视角图像之间的内部相关性不够稳定。为此，该方法首先基于图像相关和非线性相关信息熵（NCIE）对多视角 SAR 图像进行聚类，以获得具有强内部相关性的多个视角集。对于每个视角集，使用多任务稀疏表示来重建其中的 SAR 图像，以获得高精度的重建。最后，使用线性加权方法融合来自不同视角集的重建误差，并根据融合误差确定目标类别。在实验中，基于 MSTAR 数据集进行了测试，结果验证了所提方法的有效性。
