Best bands selection for detection in hyperspectral processing

Abstract

We explore the role of best bands algorithms in the context of maximizing the performance of hyperspectral algorithms. Specifically, we first focus on creating an intuitive framework for how metrics quantify the distance between two spectra. Focusing on the spectral angle mapper (SAM) metric, we demonstrate how the separability of two spectra can be increased by choosing the bands that maximize the metric. This intuition about best bands analysis for SAM is extended to the generalized likelihood ratio test (GLRT) for a practical target/background detection scenario. Results are shown for a scene imaged by the HYDICE sensor demonstrating that the separability of targets and background can be increased by carefully choosing the bands for the test.