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Proceedings Paper

Bands selection and classification of hyperspectral images based on hybrid kernels SVM by evolutionary algorithm
Author(s): Yan-Yan Hu; Dong-Sheng Li
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Paper Abstract

The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.

Paper Details

Date Published: 1 April 2016
PDF: 10 pages
Proc. SPIE 9796, Selected Papers of the Photoelectronic Technology Committee Conferences held November 2015, 979616 (1 April 2016); doi: 10.1117/12.2229875
Show Author Affiliations
Yan-Yan Hu, Electronic Engineering Institute (China)
Dong-Sheng Li, Electronic Engineering Institute (China)

Published in SPIE Proceedings Vol. 9796:
Selected Papers of the Photoelectronic Technology Committee Conferences held November 2015
Weimin Bao; Yueguang Lv, Editor(s)

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