Next-Gen SAR ATR
Abstract not provided.
Publications
Phenomenology-informed techniques for machine learning with measured and synthetic SAR imagery
Abstract not provided.
Phenomenology-informed techniques for machine learning with measured and synthetic SAR imagery
Proceedings of SPIE - The International Society for Optical Engineering
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.
An informative confidence metric for ATR
Automatic or assisted target recognition (ATR) is an important application of synthetic aperture radar (SAR). Most ATR researchers have focused on the core problem of declaration-that is, detection and identification of targets of interest within a SAR image. For ATR declarations to be of maximum value to an image analyst, however, it is essential that each declaration be accompanied by a reliability estimate or confidence metric. Unfortunately, the need for a clear and informative confidence metric for ATR has generally been overlooked or ignored. We propose a framework and methodology for evaluating the confidence in an ATR system's declarations and competing target hypotheses. Our proposed confidence metric is intuitive, informative, and applicable to a broad class of ATRs. We demonstrate that seemingly similar ATRs may differ fundamentally in the ability-or inability-to identify targets with high confidence.
4 Results