Publication Type
Journal Article
Version
publishedVersion
Publication Date
11-2014
Abstract
Band selection plays an important role in identifying the most useful and valuable information contained in the hyperspectral images for further data analysis such as classification, clustering, etc. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving the NP-hard band selection problem. In this paper, we propose a formal probabilistic memetic algorithm for band selection, which is able to adaptively control the degree of global exploration against local exploitation as the search progresses. To verify the effectiveness of the proposed probabilistic mechanism, empirical studies conducted on five well-known hyperspectral images against two recently proposed state-of-the-art MAs for band selection are presented.
Keywords
Hyperspectral image, Band selection, Memetic algorithm
Discipline
Databases and Information Systems | Software Engineering | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
Soft Computing
Volume
20
Issue
12
First Page
4685
Last Page
4693
ISSN
1432-7643
Identifier
10.1007/s00500-014-1508-1
Publisher
Springer (part of Springer Nature): Springer Open Choice Hybrid Journals
Citation
FENG, Liang; TAN, Ah-hwee; LIM, Meng-Hiot; and JIANG, Si Wei.
Band selection for hyperspectral images using probabilistic memetic algorithm. (2014). Soft Computing. 20, (12), 4685-4693.
Available at: https://ink.library.smu.edu.sg/sis_research/5207
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s00500-014-1508-1
Included in
Databases and Information Systems Commons, Software Engineering Commons, Theory and Algorithms Commons