Peña-Ortega, Carolina

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Evaluation of different structured models for target detection in hyperspectral imagery
(2010) Peña-Ortega, Carolina; Vélez-Reyes, Miguel; College of Engineering; Manian, Vidya; Hunt, Shawn; Department of Electrical and Computer Engineering; Parés-Matos, Elsie I.
Target detection is an essential component in defense, security and medical applications of hyperspectral imagery. Structured and unstructured models are used to model variability of spectral signatures for the design of information extraction algorithms. In structured models, spectral variability is modeled using different geometric representations. In linear approaches, the spectral signatures are assumed to be generated by the linear combination of basis vectors. The nature of the basis vectors and its allowable linear combinations define different structured models such as linear subspaces, convex polyhedral cones, and convex hulls. This research investigates the use of these models to describe the background of hyperspectral images, and study the performance of target detection algorithms based on these models. We also study training methods and estimation of the model order for each approach. The results show that the model order is a critical parameter and that when good background target contrast exist, all models perform well.