Regression techniques for material identification in hyperspectral data

Randy S. Roberts

doi:10.1117/12.500372

18 June 2003 Regression techniques for material identification in hyperspectral data

Randy S. Roberts

Proceedings Volume 5001, Optical Engineering at the Lawrence Livermore National Laboratory; (2003) https://doi.org/10.1117/12.500372
Event: High-Power Lasers and Applications, 2003, San Jose, CA, United States

Abstract

Identification of materials in hyperspectral imagery is a fundamental analysis task. Materials are often identified by building pixel models using a library of reference spectra along with a regression technique. This paper describes several regression techniques that are useful in modeling hyperspectral pixels, demonstrates the characteristics of the algorithms on simulated data, and compares the strengths and weaknesses of the techniques

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Randy S. Roberts "Regression techniques for material identification in hyperspectral data", Proc. SPIE 5001, Optical Engineering at the Lawrence Livermore National Laboratory, (18 June 2003); https://doi.org/10.1117/12.500372

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KEYWORDS

Received signal strength

Stochastic processes

Genetic algorithms

Data modeling

Error analysis

Statistical modeling

Algorithm development

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