4 citations · 4 across the 4 of their papers we have counts for
4 papers
Fast Generation of Representative Synthetic Dataset with Salsa to Train ATR Models with Electromagnetic Couplings Data-Augmentation
Benjamin Camus, Julien Houssay, Corentin Le Barbu +3
This work focuses on training Automatic Target Recognition (ATR) models using simulated Synthetic Aperture Radar (SAR) images to circumvent the lack of real measurements. To obtain…
Combining SAR Simulators to Train ATR Models with Synthetic Data
Benjamin Camus, Julien Houssay, Corentin Le Barbu +3
This work aims to train Deep Learning models to perform Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images. To circumvent the lack of real labelled measure…
Training Deep Learning Models with Hybrid Datasets for Robust Automatic Target Detection on real SAR images
Benjamin Camus, Théo Voillemin, Corentin Le Barbu +3
In this work, we propose to tackle several challenges hindering the development of Automatic Target Detection (ATD) algorithms for ground targets in SAR images. To address the lack…
Robust SAR ATR on MSTAR with Deep Learning Models trained on Full Synthetic MOCEM data
Benjamin Camus, Corentin Le Barbu, Eric Monteux
The promising potential of Deep Learning for Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images vanishes when considering the complexity of collecting trai…