English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The promising potential of Deep Learning for Automatic Target Recognition(ATR) on Synthetic Aperture Radar (SAR) images vanishes when considering thecomplexity of collecting training datasets measurements. Simulation canovercome this issue by producing synthetic training datasets. However, becauseof the limited representativeness of simulation, models trained in a classicalway with synthetic images have limited generalization abilities when dealingwith real measurement at test time. Previous works identified a set of equallypromising deep-learning algorithms to tackle this issue. However, theseapproaches have been evaluated in a very favorable scenario with a synthetictraining dataset that overfits the ground truth of the measured test data. Inthis work, we study the ATR problem outside of this ideal condition, which isunlikely 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 fromthe real measurements. (2) We experimentally demonstrate the limits of thestate-of-the-art. (3) We show that domain randomization techniques andadversarial training can be combined to overcome this issue. We demonstratethat this approach is more robust than the state-of-the-art, with an accuracyof 75 %, while having a limited impact on computing performance duringtraining.

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