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Proceedings Paper

Optimized hyperspectral band selection using hybrid genetic algorithm and gravitational search algorithm
Author(s): Aizhu Zhang; Genyun Sun; Zhenjie Wang
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Paper Abstract

The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, 981403 (14 December 2015); doi: 10.1117/12.2209435
Show Author Affiliations
Aizhu Zhang, East China Univ. of Petroleum (China)
Genyun Sun, East China Univ. of Petroleum (China)
Zhenjie Wang, East China Univ. of Petroleum (China)


Published in SPIE Proceedings Vol. 9814:
MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing
Jianguo Liu, Editor(s)

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