Hyperspectral clutter, phenomenology, and detection algorithms

Physical growth processes give rise to a number of hyperspectral vegetation background clutter properties which degrade the ability to detect targets in such backgrounds. In order to gain insight into this complex problem a novel three-fold method is proposed: the first appeals to growth processes to produce generative models of the background clutter; the second examines the phenomenology of these models and compares it to real data; and the third devises new anomaly detectors to mitigate the effects of these background clutter properties. Studies of model cellular automata are reported here. These models aim to replicate the local conditions necessary for successful growth of the vegetation species and as a result produce spatial correlations that match real vegetation. Non-competitive and competitive growth models, in particular, are studied and produce hyperspectral images through the use of Cameosim. In general, no degrading effects of using an enhanced spectral library were observed suggesting that the dominant factor in reducing anomaly detectors effectiveness is the spatial inhomogeneity of vegetation abundances. In addition, evidence for several important properties of the hyperspectral background is also reported. These support the conclusion that vegetation background clutter distributions are non-Gaussian. Insight gleaned from these studies has been used to develop many new improved anomaly detectors and their results are also reported and bench-marked against existing algorithms.