Sensors, Vol. 23, Pages 8031: Integrating Target and Shadow Features for SAR Target Recognition

Sensors, Vol. 23, Pages 8031: Integrating Target and Shadow Features for SAR Target Recognition Sensors doi: 10.3390/s23198031 Authors: Zhiyuan Zhao Xiaorong Xue Iqra Mariam Xing Zhou Synthetic aperture radar (SAR) sensor often produces a shadow in pairs with the target due to its slant-viewing imaging. As a result, shadows in SAR images can provide critical discriminative features for classifiers, such as target contours and relative positions. However, shadows possess unique properties that differ from targets, such as low intensity and sensitivity to depression angles, making it challenging to extract depth features from shadows directly using convolutional neural networks (CNN). In this paper, we propose a new SAR image-classification framework to utilize target and shadow information comprehensively. First, we design a SAR image segmentation method to extract target regions and shadow masks. Second, based on SAR projection geometry, we propose a data-augmentation method to compensate for the geometric distortion of shadows due to differences in depression angles. Finally, we introduce a feature-enhancement module (FEM) based on depthwise separable convolution (DSC) and convolutional block attention module (CBAM), enabling deep networks to fuse target and shadow features adaptively. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that when only using target and shadow information, the published de...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research