Band reduction for hyperspectral imagery processing

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 Citations (Scopus)

Abstract

Feature reduction denotes the group of techniques that reduce high dimensional data to a smaller set of components. In remote sensing feature reduction is a preprocessing step to many algorithms intended as a way to reduce the computational complexity and get a better data representation. Reduction can be done by either identifying bands from the original subset (selection), or by employing various transforms that produce new features (extraction). Research has noted challenges in both directions. In feature selection, identifying an "ideal" spectral band subset is a hard problem as the number of bands is increasingly large, rendering any exhaustive search unfeasible. To counter this, various approaches have been proposed that combine a search algorithm with a criterion function. However, the main drawback of feature selection remains the rather narrow bandwidths covered by the selected bands resulting in possible information loss. In feature extraction, some of the most popular techniques include Principal Component Analysis, Independent Component Analysis, Orthogonal Subspace Projection, etc. While they have been used with success in some instances, the resulting bands lack a physical relationship to the data and are mostly produced using statistical strategies. We propose a new technique for feature reduction that exploits search strategies for feature selection to extract a set of spectral bands from a given imagery. The search strategy uses dynamic programming techniques to identify 'the best set" of features.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Computational Imaging VIII
Volume7533
DOIs
StatePublished - 14 May 2010
EventComputational Imaging VIII - San Jose, CA, United States
Duration: 18 Jan 201019 Jan 2010

Other

OtherComputational Imaging VIII
CountryUnited States
CitySan Jose, CA
Period18/01/1019/01/10

Fingerprint

Hyperspectral Imagery
imagery
Feature extraction
spectral bands
Processing
pattern recognition
Feature Selection
set theory
Search Strategy
dynamic programming
Feature Extraction
preprocessing
principal components analysis
Independent component analysis
remote sensing
Subset Selection
counters
Information Loss
Dynamic programming
projection

Keywords

  • Feature extraction
  • Hyperspectral imagery
  • Search methods

Cite this

Robila, S. (2010). Band reduction for hyperspectral imagery processing. In Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging VIII (Vol. 7533). [75330W] https://doi.org/10.1117/12.837953
Robila, Stefan. / Band reduction for hyperspectral imagery processing. Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging VIII. Vol. 7533 2010.
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Robila, S 2010, Band reduction for hyperspectral imagery processing. in Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging VIII. vol. 7533, 75330W, Computational Imaging VIII, San Jose, CA, United States, 18/01/10. https://doi.org/10.1117/12.837953

Band reduction for hyperspectral imagery processing. / Robila, Stefan.

Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging VIII. Vol. 7533 2010. 75330W.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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Robila S. Band reduction for hyperspectral imagery processing. In Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging VIII. Vol. 7533. 2010. 75330W https://doi.org/10.1117/12.837953