Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
An endmember-based distance for content based hyperspectral image retrieval
Pattern Recognition
Generic visual analysis for multi- and hyperspectral image data
Data Mining and Knowledge Discovery
Further results on dissimilarity spaces for hyperspectral images RF-CBIR
Pattern Recognition Letters
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The analysis of hyperspectral images on the basis of the spectral decomposition of their pixels through the so called spectral unmixing process, has applications in thematic map generation, target detection and unsupervised image segmentation. The critical step is the determination of the endmembers used as the references for the unmixing process. We give a comprehensive enumeration of the methods used in practice, because of its implementation in widely used software packages, and those published in the literature. We have structured the review according to the basic computational approach followed by the algorithms: those based on the computational geometry formulation, the ones following lattice computing ideas and heuristic approaches with a weak formal foundation.