Invariant identification of material mixtures in airborne spectrometer data.
We present an algorithm for identifying linear mixtures of a specified set of materials in 0.4-2.5 microm airborne imaging spectrometer data. The algorithm is invariant to the illumination and atmospheric conditions and the relative amounts of the specified materials within a pixel. Only the spectral reflectance functions for the specified materials are required by the algorithm. Invariance over illumination and atmospheric conditions is achieved by incorporating a physical model for scene variability in the constrained optimization formulation. The algorithm also computes estimates of the amounts of the specified materials in identified mixtures. We demonstrate the effectiveness of the algorithm by using real and synthetic Hyperspectral Digital Imaging Collection Experiment imagery acquired over a range of conditions and altitudes.
Suen
Pei-hsiu
PH
Department of Electrical and Computer Engineering, University of California, Irvine 92697, USA. psuen@ece.uci.edu
Healey
Glenn
G