Name
Abstract | ||
|---|---|---|
In this paper, endmember extraction algorithm is described as a combinatorial optimization problem. A novel quantumbehaved particle swarm optimization (QPSO) approach which employs quantum-behaved particle swarm optimization to find endmembers with good performance is proposed. As far as our knowledge, it is the first time that quantum-behaved particle swarm optimization is introduced into hyperspectral endmember extraction. In order to follow the law of particle movement, a high dimensional particles definition is proposed. The proposed algorithm was tested and evaluated by both synthetic and real hyperspectral data sets. Experimental results indicate that the proposed method get a better result compared to the algorithms of vertex component analysis (VCA), N-FINDR and discrete particle swarm optimization (D-PSO). |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7730833 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
hyperspectral, endmember extraction, quantum-behaved particle swarm optimization | Convergence (routing),Endmember,Computer science,Artificial intelligence,Metaheuristic,Particle swarm optimization,Computer vision,Mathematical optimization,Algorithm design,Meta-optimization,Algorithm,Multi-swarm optimization,Hyperspectral imaging | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingming Xu | 1 | 21 | 5.40 |
| Liangpei Zhang | 2 | 5448 | 307.02 |
| Bo Du | 3 | 1662 | 130.01 |
| Lefei Zhang | 4 | 840 | 47.83 |
| Yuxiang Zhang | 5 | 167 | 15.28 |