NDLI: Hyperspectral band selection using crossover-based gravitational search algorithm

Content Provider	IET Digital Library
Author	Zhang, Aizhu Ma, Ping Liu, Sihan Sun, Genyun Huang, Hui Zabalza, Jaime Wang, Zhenjie Lin, Chengyan
Abstract	Band selection is an important data dimensionality reduction tool in hyperspectral images (HSIs). To identify the most informative subset band from the hundreds of highly corrected bands in HSIs, a novel hyperspectral band selection method using a crossover-based gravitational search algorithm (CGSA) is presented in this study. In this method, the discriminative capability of each band subset is evaluated by a combined optimisation criterion, which is constructed based on the overall classification accuracy and the size of the band subset. As the evolution of the criterion, the subset is updated using the V-shaped transfer function-based CGSA. Ultimately, the band subset with the best fitness value is selected. Experiments on two public hyperspectral datasets, i.e. the Indian Pines dataset and the Pavia University dataset, have been conducted to test the performance of the proposed method. Comparing experimental results against the basic GSA and the PSOGSA (hybrid PSO and GSA) revealed that all of the three GSA variants can considerably reduce the band dimensionality of HSIs without damaging their classification accuracy. Moreover, the CGSA shows superiority on both the effectiveness and efficiency compared to the other two GSA variants.
Starting Page	280
Ending Page	286
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5362
Journal	IET Image Processing
Publisher Date	2018-07-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Band Dimensionality Band Subset Best Fitness Value Classification Accuracy Combinatorial Mathematics Combined Optimisation Criterion Computer Vision And Image Processing Technique Crossover-based Gravitational Search Algorithm Data Dimensionality Reduction Tool Data Reduction GSA Variants HSIs Hybrid PSO Hyperspectral Image Image Classification Image Recognition Indian Pines Dataset Informative Subset Band Novel Hyperspectral Band Selection Method Optimisation Technique Particle Swarm Optimisation Pavia University Dataset PSOGSA Public Hyperspectral Datasets Search Problem Transfer Function Transfer Function-based CGSA V-shaped Transfer Function
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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