Characterization of hyperspectral data using a genetic algorithm

• Published in: Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154) Vol. 1; pp. 169 - 173 vol.1
• Main Author: Roberts, R.S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2000
• Subjects: Algorithm design and analysis; Data analysis; Genetic algorithms; Hyperspectral imaging; Instruments; Laboratories; Layout; Least squares methods; Libraries; Spectral analysis
• ISBN: 9780780365148; 0780365143
• ISSN: 1058-6393; 2576-2303
• DOI: 10.1109/ACSSC.2000.910937

Spectral mixture analysis plays an important role in analyzing hyperspectral data. Mixture models are typically built from spectral libraries with model coefficients determined by least squares techniques. As a result of noise, instrument artifacts, and other factors, the mixture model can be inadequate. This paper reports a genetic algorithm that generates multiple mixture models using subsets of reference spectra. Additionally, models of similar fit are also generated thereby providing the analyst with alternative explanations of the data. Details of the algorithm are provided along with some initial results of applying the algorithm to AVIRIS data.