Vehicle Target Recognition in SAR Images with Complex Scenes Based on Mixed Attention Mechanism

• Published in: Information (Basel) Vol. 15; no. 3; p. 159
• Main Authors: Tang, Tao, Cui, Yuting, Feng, Rui, Xiang, Deliang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2024
• Subjects: Accuracy; Algorithms; Artificial neural networks; Computer vision; Feature maps; Feature selection; MA-MobileNetV2; Machine learning; Machine vision; mixed attention mechanism; Modules; Neural networks; Synthetic aperture radar; synthetic aperture radar (SAR); Target recognition; vehicle target recognition
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info15030159

With the development of deep learning in the field of computer vision, convolutional neural network models and attention mechanisms have been widely applied in SAR image target recognition. The improvement of convolutional neural network attention in existing SAR image target recognition focuses on spatial and channel information but lacks research on the relationship and recognition mechanism between spatial and channel information. In response to this issue, this article proposes a hybrid attention module and introduces a Mixed Attention (MA) mechanism module in the MobileNetV2 network. The proposed MA mechanism fully considers the comprehensive calculation of spatial attention (SPA), channel attention (CHA), and coordinated attention (CA). It can input feature maps for comprehensive weighting to enhance the features of the regions of interest, in order to improve the recognition rate of vehicle targets in SAR images.The superiority of our algorithm was verified through experiments on the MSTAR dataset.