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Endmember Extraction of Hyperspectral Remote Sensing Images Based on an Improved Discrete Artificial Bee Colony Algorithm and Genetic Algorithm
- Source :
- Mobile Networks and Applications. 25:1033-1041
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Aiming at improving the performance of the endmember extraction problem in hyperspectral images, a new extraction method based on discrete hybrid artificial bee colony algorithm and genetic algorithm (DABC_GA) is proposed. By analyzing the characteristic of the problem, each dimension of candidate solution is a discrete and exclusive integer. Then we employ an optimization method with integral coding. By inheriting the strong exploration ability of the traditional artificial bee colony algorithm (ABC), we propose a discrete ABC which could quickly obtain more valuable endmembers combinations in the early stage. Then we select some outstanding results of DABC as the potential solutions of GA, which is adopted as another optimization tool in the later stage of iteration. The concept of complementary sets is proposed in the cross and mutation operators to guarantee the diversity and completeness of solutions. Meanwhile, the greedy strategy is adopted to ensure that the favorable potential solutions are not discarded. Compared with conventional extraction algorithms in simulated and real hyperspectral remote sensing data, the experimental results show the validity of our proposed algorithm.
- Subjects :
- Endmember
Mutation operator
Computer Networks and Communications
Computer science
Hyperspectral imaging
020206 networking & telecommunications
02 engineering and technology
Artificial bee colony algorithm
Hardware and Architecture
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Extraction methods
Computer communication networks
Software
Information Systems
Coding (social sciences)
Remote sensing
Subjects
Details
- ISSN :
- 15728153 and 1383469X
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- Mobile Networks and Applications
- Accession number :
- edsair.doi...........cfbb6c7f2cfd30b1b862e60408b5adcc