Affiliations: [a] School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China | [b] School of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China | [c] College of Computer and Information Engineering, Hohai University, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author: Quansen Sun, School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China. E-mail:[email protected]
Abstract: Evolutionary algorithms have been widely used in band
selection for hyperspectral images. The particle swarm optimization (PSO)
and the differential evolution (DE) algorithms are two common evolutionary
techniques with efficient optimization capabilities. In order to fully
utilize the advantages they provide, a band selection method is proposed
based on the two algorithms with hybrid encoding. This method firstly uses
hybrid encoding to make PSO and DE suitable for band selection. Secondly,
the classification accuracy of an SVM classifier is used as the fitness
function. Thirdly, we adopt the double population parallel iterative method
to search for the optimal band combination. The experimental results on
AVIRIS hyperspectral data show that the average classification accuracy of
our proposed method is higher than the binary PSO algorithm, higher than the
hybrid particle swarm algorithm, and higher than the hybrid coding
differential evolution algorithm. These classification results demonstrate
the effectiveness of the proposed method.
Keywords: Hyperspectral images, band selection, particle swarm optimization, differential evolution algorithm, support vector machine
