Related papers: Robust SAR ATR on MSTAR with Deep Learning Models trained on Full Synthetic MOCEM data

Robust SAR ATR on MSTAR with Deep Learning Models trained on Full Synthetic MOCEM data

URL: http://arxiv.org/abs/2206.07352v1
Date: Wed, 15 Jun 2022 08:04:36 GMT
Title: Robust SAR ATR on MSTAR with Deep Learning Models trained on Full Synthetic MOCEM data
Authors: Benjamin Camus, Corentin Le Barbu, Eric Monteux
Abstract summary: Simulation can overcome this issue by producing synthetic training datasets. We show that domain randomization techniques and adversarial training can be combined to overcome this issue.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The promising potential of Deep Learning for Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images vanishes when considering the complexity of collecting training datasets measurements. Simulation can overcome this issue by producing synthetic training datasets. However, because of the limited representativeness of simulation, models trained in a classical way with synthetic images have limited generalization abilities when dealing with real measurement at test time. Previous works identified a set of equally promising deep-learning algorithms to tackle this issue. However, these approaches have been evaluated in a very favorable scenario with a synthetic training dataset that overfits the ground truth of the measured test data. In this work, we study the ATR problem outside of this ideal condition, which is unlikely to occur in real operational contexts. Our contribution is threefold. (1) Using the MOCEM simulator (developed by SCALIAN DS for the French MoD/DGA), we produce a synthetic MSTAR training dataset that differs significantly from the real measurements. (2) We experimentally demonstrate the limits of the state-of-the-art. (3) We show that domain randomization techniques and adversarial training can be combined to overcome this issue. We demonstrate that this approach is more robust than the state-of-the-art, with an accuracy of 75 %, while having a limited impact on computing performance during training.

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