Comparative performances of CNN models for SAR Targets classification

This article focuses on the development of classification architectures for Synthetic Aperture Radar (SAR) image analysis, particularly for target recognition. Several methods based on deep artificial neural networks are explored and compared. Specifically, this work investigates the study of pretrained architectures such as Xception and DenseNet, which were originally developed and trained on the ImageNet database containing optical images. However, adaptation of these architectures is necessary within the context of SAR radar images. The goal was to take advantage of the powerful feature extraction capabilities of these models to effectively classify objects in radar images. We evaluated and compared the classification performances of each technique using the publicly MSTAR dataset. This led to the proposal and development of new architectures based on the Xception and DenseNet models. Using these models, it is possible to achieve impressive recognition rates close to ${9 9. 5 \%}$ on the test dataset, surpassing several benchmarks reported in the scientific literature on the same dataset.