Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is a key technique of remote-sensing image recognition, which can be supported by deep neural networks. The existing works of SAR ATR mostly focus on improving the accuracy of the target recognition while ignoring the system's performance in terms of speed and storage, which is critical to real-world applications of SAR ATR. For decision-makers aiming to identify a proper deep learning model to deploy in a SAR ATR system, it is important to understand the performance of different candidate deep learning models and determine the best model accordingly. This paper comprehensively benchmarks several advanced deep learning models for SAR ATR with multiple distinct SAR imagery datasets. Specifically, we train and test five SAR image classifiers based on Residual Neural Networks (ResNet18, ResNet34, ResNet50), Graph Neural Network (GNN), and Vision Transformer for Small-Sized Datasets (SS-ViT). We select three datasets (MSTAR, GBSAR, and SynthWakeSAR) that offer heterogeneity. We evaluate and compare the five classifiers concerning their classification accuracy, runtime performance in terms of inference throughput, and analytical performance in terms of number of parameters, number of layers, model size and number of operations. Experimental results show that the GNN classifier outperforms with respect to throughput and latency. However, it is also shown that no clear model winner emerges from all of our chosen metrics and a "one model rules all" case is doubtful in the domain of SAR ATR.