Evolutionary approaches for endmember extraction in hyperspectral unmixing using genetic algorithms
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2019-08-05
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Universidade Federal de Goiás
Resumo
Endmember Extraction is a critical step in hyperspectral unmixing and classification
providing the basis for applications such as identification of minerals, vegetation analysis, geographical survey, disaster management and target identification in military
applications. The endemember extraction determines the basic constituent materials
contained in the hyperspectral image while providing the requirements to the abundance inversion process, used to obtain the percentage of several endmembers in each
pixel. Nevertheless, low spatial resolution and computing time are the two major difficulties, the first due to the spatial interactions of different fractions of mixed endmembers and the second due to strict and extensive search utilized in state-of-the-art methods. Three evolutionary endmember extractors are proposed, so-called GAEE, GAEEIVFm and GAEEII. The first is based on solving a linear endmember extraction problem
as an evolutionary optimization task, maximizing the simplex volume in the endmember search space, GAEE-IVFm represents a variation with of the GAEE with an In Vitro
Fertilization module, and the GAEEII is a multi-epochs genetic algorithm with enhancements to the naive genetic algorithm endmember extractor (GAEE). To demonstrate
the superiority of the proposed methods, extensive experiments are conducted on several well-known real and synthetic hyperspectral images, as well as a possible relationship between the spectral angle distance (SAD) and the volume of the simplex. The
results confirm that the proposed methods considerably improved, up to three times
increase in accuracy and scalable computing time compared to the state-of-the-art
techniques in the literature including recent developments.
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SOARES, Douglas Winston Ribeiro. Evolutionary approaches for endmember extraction in hyperspectral unmixing using genetic algorithms. 2019. 66 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2019.