Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 49, Issue 11, Pages 4173-4176Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2011.2131145
Keywords
Endmember extraction; hyperspectral remote sensing; particle swarm optimization (PSO)
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Funding
- National Basic Research Program of China (973) [2009CB723902]
- National High Technology Research and Development Program of China (863) [2008AA12Z113]
- National Natural Science Foundation of China [40901225, 40801127]
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This paper described endmember extraction as a combinatorial optimization problem (COP). By defining particles' position and velocity, discrete particle swarm optimization (D-PSO) was proposed based on particle swarm optimization to resolve COP. The algorithm was tested and evaluated by hyperspectral remote sensing data. Experimental results showed that, while extracting the same number of endmembers, D-PSO could get a smaller root-mean-square error between an original image and its remixed image on the precondition of correct extraction results compared to the algorithms of vertex component analysis (VCA) and N-FINDR, which meant that D-PSO could acquire higher extraction precision.
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