Zhejiang Institute of Economics and Trade, Hangzhou 310018, China.
Comput Intell Neurosci. 2021 Aug 25;2021:4392702. doi: 10.1155/2021/4392702. eCollection 2021.
A novel feature generation algorithm for the synthetic aperture radar image is designed in this study for automatic target recognition. As an adaptive 2D signal processing technique, bidimensional empirical mode decomposition is employed to generate multiscale bidimensional intrinsic mode functions from the original synthetic aperture radar images, which could better capture the broad spectral information and details of the target. And, the combination of the original image and decomposed bidimensional intrinsic mode functions could promisingly provide more discriminative information for correct target recognition. To reduce the high dimension of the original image as well as bidimensional intrinsic mode functions, multiset canonical correlations analysis is adopted to fuse them as a unified feature vector. The resultant feature vector highly reduces the high dimension while containing the inner correlations between the original image and decomposed bidimensional intrinsic mode functions, which could help improve the classification accuracy and efficiency. In the classification stage, the support vector machine is taken as the basic classifier to determine the target label of the test sample. In the experiments, the 10-class targets in the moving and stationary target acquisition and recognition dataset are classified to investigate the performance of the proposed method. Several operating conditions and reference methods are setup for comprehensive evaluation.
本研究设计了一种新的合成孔径雷达图像特征生成算法，用于自动目标识别。二维经验模态分解作为一种自适应二维信号处理技术，从原始合成孔径雷达图像中生成多尺度二维固有模态函数，能够更好地捕获目标的宽谱信息和细节。并且，原始图像与分解的二维固有模态函数的组合可以为正确的目标识别提供更具判别力的信息。为了降低原始图像和二维固有模态函数的高维性，采用多集典型相关分析将它们融合为统一的特征向量。生成的特征向量在降低高维性的同时，包含了原始图像和分解的二维固有模态函数之间的内在相关性，有助于提高分类精度和效率。在分类阶段，支持向量机作为基本分类器来确定测试样本的目标标签。在实验中，对运动和静止目标获取和识别数据集的 10 类目标进行分类，以研究所提出方法的性能。设置了几种工作条件和参考方法进行综合评估。
