Deep Learning Architectures for Accurate Target Detection in Synthetic Aperture RADAR

This paper presents an in-depth analysis of Synthetic Aperture Radar (SAR) image processing, with a focus on utilizing deep learning techniques for military vehicle identification, particularly tanks. SAR images have long been integral to the Joint Intelligence, Surveillance, and Reconnaissance (JISR) field. Building on this foundation, we propose a novel approach using deep learning architectures, namely Convolutional Neural Networks (CNN), AlexNet, and VGG16, to classify tanks into seven categories, along with a class for incorrect images. The performance of these models is thoroughly examined, with AlexN et demonstrating superior accuracy in detecting and classifying targets within SAR imagery. This study not only advances military vehicle recognition but also highlights the potential of deep learning in broader SAR-based applications, such as Automatic Target Recognition (ATR) and intelligence gathering, with implications in both military and civilian sectors.