Endmember Extraction is a critical step in hyperspectral image analysis and classification. It is an useful method to decompose a mixed spectrum into a collection of spectra and their corresponding proportions. In this paper, we solve a linear endmember extraction problem as an evolutionary optimization task, maximizing the Simplex Volume in the endmember space. We propose a standard genetic algorithm and a variation with In Vitro Fertilization module (IVFm) to find the best solutions and compare the results with the state-of-art Vertex Component Analysis (VCA) method and the traditional algorithms Pixel Purity Index (PPI) and N-FINDR. The experimental results on real and synthetic hyperspectral data confirms the overcome in performance and accuracy of the proposed approaches over the mentioned algorithms.

Paper Online in https://arxiv.org/abs/1805.10644

@ARTICLE{2018arXiv180510644D,
   author = {{Douglas Winston.~R.}, S. and {Laureano}, G.~T. and {Camilo}, Jr, C.~G.
	},
    title = "{Comparison of VCA and GAEE algorithms for Endmember Extraction}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1805.10644},
 keywords = {Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Image and Video Processing, 68T20, 68U10},
     year = 2018,
    month = may,
   adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180510644D},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Envirionment Setup:

Monte Carlo runs: 50

Number of endmembers to estimate: 12

Number of skewers (PPI): 1000

Maximum number of iterations (N-FINDR): 36