A Novel Detection Paradigm and Its Comparison to Statistical and Kernel-Based Anomaly Detection Algorithms for Hyperspectral Imagery

Colin C. Olson, Timothy Doster; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 108-114

Detection of anomalous pixels within hyperspectral imagery is frequently used for purposes ranging from the location of invasive plant species to the detection of military targets. The task is unsupervised because no information about target or background spectra is known or assumed. Some of the most commonly used detection algorithms assume a statistical distribution for the background and rate spectral anomalousness based on measures of deviation from the statistical model; but such assumptions can be problematic because hyperspectral data rarely meet them. More recent algorithms have employed data-driven machine learning techniques in order to improve performance. Here we investigate a novel kernel-based method and show that it achieves top detection performance relative to seven other state-of-the-art methods on a commonly tested data set.