Using colorization to bridge the synthetic-measured gap

Jeremy Cavallo; Brian Rigling

doi:10.1117/12.3013696

7 June 2024 Using colorization to bridge the synthetic-measured gap

Jeremy Cavallo, Brian Rigling

Proceedings Volume 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI; 130320M (2024) https://doi.org/10.1117/12.3013696
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

This paper explores the use of colorization as a data augmentation and its applications in bridging the synthetic-measured gap. A current problem in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) is training deep learning networks on largely synthetic data and transferring the knowledge to the measured domain. Data augmentations, such as colorization, can make the deep learning models more robust to the shift in domain when used during training, leading to improved performance over traditional synthetic data. Our approach utilizes a lossless colorization augmentation and applies it to various ResNet-based architectures1 to improve the SAR ATR performance when trained on limited measured data.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jeremy Cavallo and Brian Rigling "Using colorization to bridge the synthetic-measured gap", Proc. SPIE 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI, 130320M (7 June 2024); https://doi.org/10.1117/12.3013696

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