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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