Deep Learning-Based Strategies and Optimization Methods for SAR ATR

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is a challenging and critical task in remote sensing applications. Deep Convolutional Neural Networks (CNNs) are capable of high-level feature mining and extraction, making them well-suited for unified end-to-end tasks. However, specific imaging data can pose certain challenges and bottlenecks. This paper lists the difficulties that CNN-based detection models face during localization and classification in SAR images. Several optimization methods and improvement strategies that can promote SAR ATR tasks have been investigated. Finally, combining the advantages of CNN models and the power of attention mechanism borrowed from the Vision Transformer (ViT) has become an innovative research topic addressing object detection challenges.