Nonlinear Hyperspectral Unmixing With Graphical Models [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2019 In optical remote sensing, phenomena such as multiple scattering, shadowing, and spatial neighbor effects generate spectral reflectances that are nonlinear mixtures of the reflectances of the surface materials. Using hyperspectral images, the obtained spectral reflectances can be unmixed. We present a general method for creating nonlinear mixing models,
Rob Heylen +4 more
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On the use of overcomplete dictionaries for spectral unmixing [PDF]
, 2012 Hyperspectral unmixing is a sub pixel classification method which aims at recovering fraction and type of materials mixed in a single pixel. This work addresses the unmixing problem from the compressive sensing point of view by using overcomplete ...
Xiaoxiang Zhu +7 more
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Implementation strategies for hyperspectral unmixing using Bayesian source separation. [PDF]
, 2010 Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing. Moreover, it is sensible to impose a sum-to-one (full
Moussaoui, Saïd +11 more
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Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery [PDF]
, 2009 This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing ...
Moussaoui, Saïd +10 more
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Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery [PDF]
, 2008 This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive ...
Chang, Chein-I +5 more
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Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]
, 2013 Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Eches, Olivier +3 more
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Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model [PDF]
, 2012 This paper studies a nonlinear mixing model for hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian
Altmann, Yoann +3 more
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Hyperspectral Images Unmixing Based on Abundance Constrained Multi-Layer KNMF
IEEE Access, 2021 Due to the low spatial resolution of the sensors, the hyperspectral images contain mixed pixels. The purpose of hyperspectral unmixing is to decompose the mixed pixels into a series of endmembers and abundance fractions.
Jing Liu, You Zhang, Yi Liu, Caihong Mu
doaj +1 more source
Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery [PDF]
, 2010 This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers.
Eches, Olivier +3 more
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Hyperspectral Unmixing Hierarchies
CoRRUnmixing reveals the spatial distribution and spectral details of different constituents, called endmembers, in a hyperspectral image. Because unmixing has limited ground truth requirements, can accommodate mixed pixels, and is closely tied to light propagation, it is a uniquely powerful tool for analyzing hyperspectral images.
Joseph L. Garrett +6 more
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