Work smarter, not harder: simple classifiers can make simple work of common SAR ATR datasets

Deep neural networks are powerful tools that allow one to achieve state of the art performance for target recognition on SAR. Recent literature on SAR target recognition has heavily pursued the use of large, complex neural networks trained on increasingly larger classification datasets. To justify the use of these models, a common assumption is that SAR datasets are sufficiently difficult to learn. Here, we explore this basis. By using classifiers like K-Nearest Neighbor in conjunction with simple image processing, we first show that basic ML models can achieve similar accuracies to DNNs. These accuracies give a baseline for evaluating how much complex models help for improving accuracy on SAR target recognition. Overall, our findings motivate new questions around the cost benefit tradeoffs of large models and the true difficulty of the common benchmark datasets.