Unsupervised Techniques for SAR Imagery: Feature Learning, Encoding, and Extraction Toolkit (FLEET)

To support geospatial intelligence (GEOINT) through exploitation and analysis of synthetic aperture radar (SAR) imagery and address the challenges created by task-specific classification methodologies where feature extraction is mission-limited, ARiA will utilize deep learning (DL) expertise to enable automated unsupervised feature extraction (AUFE). Building upon our development of unsupervised DL for classification, segmentation, and augmentation of remote-sensing imagery ARiA will develop and demonstrate the feasibility of the Feature Learning, Encoding, and Extraction Toolkit (FLEET), a feature-extraction software for use with SAR that: (1) intelligently learns robust encodings from unlabeled data, thereby (2) reduces manual feature engineering, and (3) provides a framework for feature visualization and analysis (FVA). Fleet will facilitate a future National Geospatial-Intelligence Agency (NGA) capability for high performance GEOINT to support mission critical tasks.