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Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
2022
Applied Sciences
The existence of intra-class spectral variability caused by differential scene components and illumination conditions limits the improvement of endmember extraction accuracy, as most endmember extraction algorithms directly find pixels in the hyperspectral image as endmembers. This paper develops a quadratic clustering-based simplex volume maximization (CSVM) approach to effectively alleviate spectral variability and extract endmembers. CSVM first adopts spatial clustering based on simple
doi:10.3390/app12147132
fatcat:onrnn27qj5b6bctdrurn5wdndq