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Presentation + Paper
12 April 2021 Phenomenology-informed techniques for machine learning with measured and synthetic SAR imagery
Author Affiliations +
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
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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