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Discriminant absorption-feature learning for material classification
journal contribution
posted on 2011-05-01, 00:00 authored by Zhouyu Fu, Antonio Robles-KellyAntonio Robles-KellyIn this paper, we develop a novel approach to object-material identification in spectral imaging by combining the use of invariant spectral absorption features and statistical machine-learning techniques. Our method hinges on the relevance of spectral absorption features for material identification and casts the problem into a pattern-recognition setting by making use of an invariant representation of the most discriminant band segments in the spectra. Thus, here, we view the identification problem as a classification task, which is effected based upon those invariant absorption segments in the spectra which are most discriminative between the materials under study. To robustly recover those bands that are most relevant to the identification process, we make use of discriminant learning. To illustrate the utility of our method for purposes of material identification, we perform experiments on both terrestrial and remotely sensed hyperspectral imaging data and compare our results to those yielded by an alternative.
History
Journal
IEEE transactions on geoscience and remote sensingVolume
49Issue
5Pagination
1536 - 1556Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
ISSN
0196-2892eISSN
1558-0644Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2010, IEEEUsage metrics
Categories
Keywords
Absorption-band detectionclassificationfeature selection\/extractionhyperspectral image analysisphotometric invarianceScience & TechnologyPhysical SciencesTechnologyGeochemistry & GeophysicsEngineering, Electrical & ElectronicRemote SensingImaging Science & Photographic TechnologyEngineeringSUPERVISED CLASSIFIERIMAGING SPECTROSCOPYREFLECTANCESREDUCTIONGeophysics
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