Benchmarking Deep Learning Classifiers for SAR Automatic Target
  Recognition
Jacob Fein-Ashley Tian Ye Rajgopal Kannan Viktor Prasanna Carl Busart
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 systems 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