ABSTRACT

This letter combines the multi-mode representations extracted by bidimensional empirical mode decomposition (BEMD) and deep residual networks (ResNet) for synthetic aperture radar (SAR) target recognition. Bidimensional intrinsic mode functions (BIMFs) are generated by BEMD to describe the target characteristics in SAR images. A special ResNet is designed and trained for each layer of BIMFs. The decisions from different BIMFs are linearly fused using a random weight matrix. Typical test scenarios are designed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to examine the proposed method. The results validate the validity and robustness of the method.

摘要

这封信结合了通过二维经验模式分解（BEMD）和深度残差网络（ResNet）提取的多模式表示形式，用于合成孔径雷达（SAR）目标识别。BEMD生成二维固有模式函数（BIMF），以描述SAR图像中的目标特征。针对BIMF的每一层都设计并培训了一个特殊的ResNet。使用随机权重矩阵线性融合来自不同BIMF的决策。在移动和固定目标获取与识别（MSTAR）数据集上设计了典型的测试场景，以检验所提出的方法。结果验证了该方法的有效性和鲁棒性。