A Band Selection Method for Hyperspectral Image Based on Particle Swarm Optimization Algorithm with Dynamic Sub-Swarms

Band selection is an effective means to reduce the hyperspectral data size and to overcome the Hughes phenomenon in ground object classification. This paper presents a band selection method based on particle swarm dynamic with sub-swarms optimization, aiming at the deficiency of particle swarm optim...

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Bibliographic Details
Published in:Journal of signal processing systems 2018-09, Vol.90 (8-9), p.1269-1279
Main Authors: Xu, Mengxi, Shi, Jianqiang, Chen, Wei, Shen, Jie, Gao, Hongmin, Zhao, Jia
Format: Article
Language:English
Subjects:
Algorithms
Circuits and Systems
Clustering
Computer Imaging
Electrical Engineering
Engineering
Fitness
Image Processing and Computer Vision
Optimization algorithms
Particle swarm optimization
Pattern Recognition
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Subgroups
Support vector machines
Vision
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Summary:Band selection is an effective means to reduce the hyperspectral data size and to overcome the Hughes phenomenon in ground object classification. This paper presents a band selection method based on particle swarm dynamic with sub-swarms optimization, aiming at the deficiency of particle swarm optimization algorithm being easy to fall into local optimum when applied to hyperspectral image band selection. This algorithm treats fitness function as criterion, dividing all particles into different adaptation degree interval corresponding to the dynamic subgroup and adopting different optimization methods for different subgroups as well as sub -swarms parallel iterative searching for the optimal band. In this way, we can make achievement of clustering optimization of particle with different optimization capability, ensuring the diversity of particles in order to reduce the risk of falling into local optimum. Finally, we prove the effectiveness of this algorithm through selected bands validation by support vector machine.
ISSN:1939-8018
1939-8115
DOI:10.1007/s11265-018-1348-9