2018 IEEE Congress on Evolutionary Computation (CEC) 2018
DOI: 10.1109/cec.2018.8477743
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Comparison of VCA and GAEE algorithms for Endmember Extraction

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Cited by 5 publications
(4 citation statements)
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“…After the image restructuring, the first 20 pixels belong to the first cluster, the next 30 pixels belong to the second cluster and the last 50 pixels belong to the third cluster. The lower vector is (1,21,51) and the upper vector is (20,50,100). The image restructuring process only changes the order of pixels.…”
Section: Image Restructuringmentioning
confidence: 99%
See 1 more Smart Citation
“…After the image restructuring, the first 20 pixels belong to the first cluster, the next 30 pixels belong to the second cluster and the last 50 pixels belong to the third cluster. The lower vector is (1,21,51) and the upper vector is (20,50,100). The image restructuring process only changes the order of pixels.…”
Section: Image Restructuringmentioning
confidence: 99%
“…Besides, the conventional objective function, the volume of the simplex composed of extracted endmembers, was also used in the single-objective evolutionary algorithms [40]. In the past decade, miscellaneous algorithms have been designed either to improve the efficiency or enhance the optimization results of single-objective EE [41][42][43][44][45][46][47][48][49][50][51], including a variety of evolutionary algorithms, such as PSO algorithms [32,34,[40][41][42], ACO algorithms [33,[43][44][45], genetic algorithms (GAs) [46][47], and differential evolution (DE) algorithm [48].…”
Section: Introductionmentioning
confidence: 99%
“…The vertex component analysis (VCA) algorithm [8,22,23] is an unsupervised endmember extraction algorithm and works with the assumption that, in linear spectral mixing, every pixel signature is composed with the linear combinations of endmember spectra available within the scene. The VCA algorithm explores two facts: One, the endmembers are found to be the vertices of the simplexes and two, the affine transformation of every simplex is too simplex.…”
Section: Vertex Component Analysis (Vca)mentioning
confidence: 99%
“…In [40], an improved PPI algorithm is proposed to reduce the false alarm probability as well as computational complexity by generating skewers using statistical parameters of the hyperspectral dataset. In [41], a standard genetic algorithm with a variation with In Vitro Fertilization module (IVFm) is introduced to find endmembers. The methods aforementioned are all based on the LMM, which is one of the most popular models for spectral unmixing.…”
Section: Introductionmentioning
confidence: 99%