13 May 2010 Hyperspectral sub-pixel target identification using least-angle regression
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Abstract
A novel approach to VNIR hyperspectral target identification is presented based on the Least-Angle Regression (LARS) variable selection and model building algorithm. The problem to be solved is that of accurately identifying a target's primary signature component given a sub-pixel observation. Traditional matched detectors (MF, ACE, etc.) perform well at discriminating a target from a random cluttered background, but do not perform so well at unambiguously matching an observation with its counterpart in a large spectral library containing thousands of signatures. The LARS model-building algorithm efficiently selects a parsimonious subset of a large ensemble of model terms to optimally describe a particular target observation. The LARS solution technique is a recent addition to the family of model selection algorithms that includes Stepwise Regression, Forward Selection, and Backward Elimination. LARS is particularly well-suited to this problem as it is easily modified to enforce material abundance constraints: positive coefficients that sum to unity. Other approaches generally enforce such constraints in an ad-hoc fashion or use computationally demanding nonlinear programming solution techniques. LARS enforces these constraints as an inherent property of the model while remaining as computationally efficient as traditional sequential linear least-squares solvers. We demonstrate and quantify sub-pixel material identification performance using simulated target observations tested against large signature libraries.
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Pierre V. Villeneuve, Pierre V. Villeneuve, Alex R. Boisvert, Alex R. Boisvert, Alan D. Stocker, Alan D. Stocker, } "Hyperspectral sub-pixel target identification using least-angle regression", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951V (13 May 2010); doi: 10.1117/12.850563; https://doi.org/10.1117/12.850563
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