Comparison of VCA and GAEE algorithms for Endmember Extraction
This work addresses endmember extraction for hyperspectral image analysis, offering incremental improvements over existing algorithms.
The authors tackled the linear endmember extraction problem in hyperspectral image analysis by formulating it as an evolutionary optimization task to maximize simplex volume, proposing a standard genetic algorithm and a variant with an In Vitro Fertilization module. They reported that these approaches outperformed state-of-the-art methods like VCA, PPI, and N-FINDR in performance and accuracy on real and synthetic data.
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.