Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the art

This work concerns a detailed review of data analysis methods used for remotely sensed images of large areas of the Earth and of other solid astronomical objects. Focus is on the problem of inferring the materials that cover the surfaces captured by hyper-spectral images and estimating their abundances and spatial distributions within the region. Different hyper-spectral unmixing methods are reported as well as compared. The most important public data-sets in this setting, which are vastly used in the testing and validation of the former, are also systematically explored. Typically, a pixel-wise constrained regression is used assuming linear mixing. Yet, more recent methodologies go beyond such assumption and are thus analysed. Data-based testing of assumptions and uncertainty quantification are found to be scarce in the literature. Open problems are spotlighted and concrete recommendations for future research are provided.