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

TL;DR

This review paper comprehensively analyzes current hyper-spectral unmixing algorithms for remote surface mapping, comparing methods, datasets, and highlighting open challenges to guide future research in Earth and astronomical imaging.

Contribution

It provides a systematic comparison of state-of-the-art unmixing algorithms, reviews key datasets, and offers insights into open problems and future directions.

Findings

Most successful algorithms are identified and compared.

Key public datasets for validation are systematically reviewed.

Open problems and future research recommendations are highlighted.

Abstract

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. In detail, it focuses 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. The most successful and relevant hyper-spectral unmixing methods are reported as well as compared, as an addition to analysing the most recent methodologies. 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. Finally, open problems are spotlighted and concrete recommendations for future research are provided.