Publications

Abstract not provided.

Abstract not provided.

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.

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.