Band Selection for Hyperspectral Image Classification Based on Improved Particle Swarm Optimization Algorithm

Abstract:

Hyperspectral images have been widely used in earth observation. However, there are some problems such as huge amount of data and high correlation between bands. An application of particle swarm optimization algorithm based on B distance was proposed to band selection of hyperspectral images. First of all, bands are grouping by the correlation coefficient of the band and adjacent bands. B distance was used as separability criterion between classes and the fitness function comes into being. Finally, the classification results illustrate that the total classification accuracy of the proposed method is higher than the traditional method.

• Periodical: Advanced Materials Research (Volumes 889-890)
• Pages: 1073-1077
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.889-890.1073
• Online since: February 2014
• Authors: Chen Ming Li*, Yan Wang, Hong Min Gao, Li Li Zhang
• Keywords: B Distance, Band Selection, Hyperspectral Image, Particle Swarm Optimization Algorithm

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