Phenomenology-informed techniques for machine learning with measured and synthetic SAR imagery

Christopher P. Walker; Kelsie M. Larson; Ireena A. Erteza; Brian K. Bray

doi:10.1117/12.2587835

12 April 2021 Phenomenology-informed techniques for machine learning with measured and synthetic SAR imagery

Christopher P. Walker, Kelsie M. Larson, Ireena A. Erteza, Brian K. Bray

Proceedings Volume 11728, Algorithms for Synthetic Aperture Radar Imagery XXVIII; 1172807 (2021) https://doi.org/10.1117/12.2587835
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Phenomenology-Informed (PI) Machine Learning is introduced to address the unique challenges faced when applying modern machine-learning object recognition techniques to the SAR domain. PI-ML includes a collection of data normalization and augmentation techniques inspired by successful SAR ATR algorithms designed to bridge the gap between simulated and real-world SAR data for use in training Convolutional Neural Networks (CNNs) that perform well in the low-noise, feature-dense space of camera-based imagery. The efficacy of PI-ML will be evaluated using ResNet, EfficientNet, and other networks, using both traditional training techniques and all-SAR transfer learning.

Conference Presentation

Citation Download Citation

Christopher P. Walker, Kelsie M. Larson, Ireena A. Erteza, and Brian K. Bray "Phenomenology-informed techniques for machine learning with measured and synthetic SAR imagery", Proc. SPIE 11728, Algorithms for Synthetic Aperture Radar Imagery XXVIII, 1172807 (12 April 2021); https://doi.org/10.1117/12.2587835

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