Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms

By Nicolas Dobigeon, Jean-Yves Tourneret, Cédric Richard, José Carlos Moreira Bermudez, S. Mclaughlin, A. Hero. IEEE Signal Processing Magazine, 2013

453 citations recorded by the source corpus.

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid, and other nonlinear models need to be considered, for instance, when there are multiscattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this article, we present an overview of recent advances in nonlinear unmixing modeling.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/1a0cd26f468f1ce8fe6dd5021efb1c4e0e5a5fb7

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “NONLINEAR UNMIXING Apart from the linear spectral unmixing algorithms, several alternatives dealing with the mixture problem can be found in the literature [166]–[168].”
A Review of Nonlinear Hyperspectral Unmixing Methods (2014, methodology): “This bilinear model differs from the Fan and generalized bilinear models on two accounts: selfinteractions are included, and all bilinear terms are scaled with the same constant: ∀m, k : bmk = bamak.”