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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 13 — May. 1, 2014
  • pp: 2839–2846

Feature weighting algorithms for classification of hyperspectral images using a support vector machine

Bin Qi, Chunhui Zhao, and Guisheng Yin  »View Author Affiliations


Applied Optics, Vol. 53, Issue 13, pp. 2839-2846 (2014)
http://dx.doi.org/10.1364/AO.53.002839


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Abstract

The support vector machine (SVM) is a widely used approach for high-dimensional data classification. Traditionally, SVMs use features from the spectral bands of hyperspectral images with each feature contributing equally to the classification. In practical applications, although affected by noise, slight contributions can also be obtained from deteriorated bands. Thus, compared with feature reduction or equal assignment of weights to all the features, feature weighting is a trade-off choice. In this study, we examined two approaches to assigning weights to SVM features to increase the overall classification accuracy: (1) “CSC-SVM” refers to a support vector machine with compactness and a separation coefficient feature weighting algorithm, and (2) “SE-SVM” refers to a support vector machine with a similarity entropy feature weighting algorithm. Analyses were conducted on a public data set with nine selected land-cover classes. In comparison with traditional SVMs and other classical feature weighting algorithms, the proposed weighting algorithms increase the overall classification accuracy, and even better results could be obtained with few training samples.

© 2014 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(110.2960) Imaging systems : Image analysis
(280.0280) Remote sensing and sensors : Remote sensing and sensors

ToC Category:
Image Processing

History
Original Manuscript: December 18, 2013
Revised Manuscript: March 3, 2014
Manuscript Accepted: March 31, 2014
Published: April 25, 2014

Citation
Bin Qi, Chunhui Zhao, and Guisheng Yin, "Feature weighting algorithms for classification of hyperspectral images using a support vector machine," Appl. Opt. 53, 2839-2846 (2014)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-13-2839


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