Hyperspectral Sub-Pixel Target Identification usingLeast-Angle Regression

摘要

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'sprimary signature component given a sub-pixel observation. Traditional matched detectors (MF, ACE, etc.) performwell at discriminating a target from a random cluttered background, but do not perform so well at unambiguouslymatching an observation with its counterpart in a large spectral library containing thousands of signatures. The LARSmodel-building algorithm efficiently selects a parsimonious subset of a large ensemble of model terms to optimallydescribe a particular target observation. The LARS solution technique is a recent addition to the family of modelselection algorithms that includes Stepwise Regression, Forward Selection, and Backward Elimination. LARS isparticularly well-suited to this problem as it is easily modified to enforce material abundance constraints: positivecoefficients that sum to unity. Other approaches generally enforce such constraints in an ad-hoc fashion or usecomputationally demanding nonlinear programming solution techniques. LARS enforces these constraints as an inherentproperty 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 testedagainst large signature libraries.