Paper
27 January 2010 Band reduction for hyperspectral imagery processing
Author Affiliations +
Proceedings Volume 7533, Computational Imaging VIII; 75330W (2010) https://doi.org/10.1117/12.837953
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States
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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stefan A. Robila "Band reduction for hyperspectral imagery processing", Proc. SPIE 7533, Computational Imaging VIII, 75330W (27 January 2010); https://doi.org/10.1117/12.837953
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Cited by 2 scholarly publications.
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KEYWORDS
Detection and tracking algorithms

Feature selection

Image processing

Baryon acoustic oscillations

Hyperspectral imaging

Feature extraction

Neodymium

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