Comparison of VCA and GAEE algorithms for Endmember Extraction

05/27/2018 ∙ by Douglas Winston. R. S., et al. ∙ 0 ∙ share

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.

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Comparison of VCA and GAEE algorithms for Endmember Extraction

Authors

Endmember Extraction on the Grassmannian

Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images

Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity

Deep Clustering With Intra-class Distance Constraint for Hyperspectral Images

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability

Matrix Cofactorization for Joint Representation Learning and Supervised Classification -- Application to Hyperspectral Image Analysis

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