Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the art
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 81 references that are in the map. First posted on arXiv 18 Jul 2025.
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6 of 81 references.
References · 6
- 2019Deep Learning for Hyperspectral Image Classification: An Overviewcited by 123
- 2018An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixingcited by 66
- 2020Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Reviewcited by 46
- 2013Nonlinear unmixing of hyperspectral images: models and algorithmscited by 27
- 2025UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Constructioncited by 8
- 2021Spatial Functional Data Modeling of Plant Reflectancescited by 1
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