Compressive Hyperspectral Imaging and Anomaly Detection

Abstract : We have developed and applied successfully new algorithms for hyperspectral imagery. These include compressive sensing, anomaly detection, target detection, endmember detection, unmixing and change detection. These were tested on data provided by AFRL with good results, including change detection under different lightning conditions. Ideas involved Bregman iteration applied to L1 and total variation based optimizations were used and also successfully applied to subsampled data. A nonnegative matrix factorization and completion algorithm was introduced which allows the reconstruction of partially observed or corrupted hyperspectral data. A surprising spinoff is sparse reconstruction of offshore oil spills based on multispectral measurements.