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   Articles with "hyperspectral unmixing" as a keyword



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Spatially adaptive hyperspectral unmixing through endmembers analytical localization based on sums of anisotropic 2D Gaussians

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recommendations! Published in 2018 at "Isprs Journal of Photogrammetry and Remote Sensing"

DOI: 10.1016/j.isprsjprs.2018.03.021

Abstract: Abstract Spectral unmixing provides, for each pixel in the image, an estimated vector of fractional abundances that correspond to pure signatures, known as endmembers (EMs). Standard unmixing techniques rely only on spectral information and each… read more here.

Keywords: spatially adaptive; anisotropic gaussians; hyperspectral unmixing; unmixing endmembers ... See more keywords
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Kernel sparse representation for hyperspectral unmixing based on high mutual coherence spectral library

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recommendations! Published in 2019 at "International Journal of Remote Sensing"

DOI: 10.1080/01431161.2019.1666215

Abstract: ABSTRACT Sparse regression is now a popular method for hyperspectral unmixing relying on a prior spectral library. However, it is limited by the high mutual coherence spectral library which contains high similarity atoms. In order… read more here.

Keywords: mutual coherence; hyperspectral unmixing; high mutual; coherence spectral ... See more keywords
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Hyperspectral unmixing using deep convolutional autoencoder

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recommendations! Published in 2020 at "International Journal of Remote Sensing"

DOI: 10.1080/01431161.2020.1724346

Abstract: ABSTRACT Hyperspectral Unmixing (HU) estimates the combination of endmembers and their corresponding fractional abundances in each of the mixed pixels in the hyperspectral remote sensing image. In this paper, we address the linear unmixing problem… read more here.

Keywords: unmixing using; deep convolutional; hyperspectral unmixing; convolutional autoencoder ... See more keywords

Endmembers compression based nonnegative matrix factorization for hyperspectral unmixing

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recommendations! Published in 2023 at "International Journal of Remote Sensing"

DOI: 10.1080/01431161.2023.2195572

Abstract: ABSTRACT Hyperspectral unmixing (HU) has grown more important in hyperspectral images (HSIs) research in recent years. Many models based on nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) have been extensively used to tackle… read more here.

Keywords: factorization; matrix factorization; based nonnegative; nonnegative matrix ... See more keywords
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A Sturdy Nonlinear Hyperspectral Unmixing

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recommendations! Published in 2020 at "Iete Journal of Research"

DOI: 10.1080/03772063.2020.1838345

Abstract: Hyperspectral unmixing (HSU) is a way to process the prediction of the existing endmembers and the fractional abundances (FA) available in all pixels in the hyperspectral images. However, in a prac... read more here.

Keywords: sturdy nonlinear; nonlinear hyperspectral; hyperspectral unmixing;
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Higher Order Nonlinear Hyperspectral Unmixing for Mineralogical Analysis Over Extraterrestrial Bodies

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recommendations! Published in 2017 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2017.2699083

Abstract: Algorithms allowing the deconvolution of hyperspectral data play a key-role in remotely sensed data processing for mineralogical investigation. Modified Gaussian model (MGM) based methods are of particular interest because they are able to retrieve accurate… read more here.

Keywords: extraterrestrial bodies; hyperspectral unmixing; higher order; order nonlinear ... See more keywords
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Parallel Implementation of a Full Hyperspectral Unmixing Chain Using OpenCL

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recommendations! Published in 2017 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2017.2707541

Abstract: Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Due to the fact that the spatial resolution of the sensor may not be able to separate different spectrally pure components (endmembers), spectral… read more here.

Keywords: implementation; hyperspectral unmixing; unmixing; chain using ... See more keywords
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Group Low-Rank Nonnegative Matrix Factorization With Semantic Regularizer for Hyperspectral Unmixing

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recommendations! Published in 2018 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2018.2805779

Abstract: In this paper, the low rank prior of abundances of hyperspectral data is explored and combined with semantic information to develop a new Group Low-rank constrained Nonnegative Matrix Factorization (GLrNMF) method for linear hyperspectral unmixing.… read more here.

Keywords: hyperspectral unmixing; nonnegative matrix; matrix factorization; low rank ... See more keywords

A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing

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recommendations! Published in 2021 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2021.3069574

Abstract: Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has… read more here.

Keywords: hyperspectral unmixing; topic relaxion; topic; model ... See more keywords
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Bilateral Joint-Sparse Regression for Hyperspectral Unmixing

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recommendations! Published in 2021 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2021.3115172

Abstract: Sparse hyperspectral unmixing has been a hot topic in recent years. Joint sparsity assumes that each pixel in a small neighborhood of hyperspectral images (HSIs) is composed of the same endmembers, which results in a… read more here.

Keywords: hyperspectral unmixing; bilateral joint; joint sparse; sparse regression ... See more keywords
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Blind Hyperspectral Unmixing Using Autoencoders: A Critical Comparison

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recommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2021.3140154

Abstract: Deep learning (DL) has heavily impacted the data-intensive field of remote sensing. Autoencoders are a type of DL methods that have been found to be powerful for blind hyperspectral unmixing (HU). HU is the process… read more here.

Keywords: critical comparison; using autoencoders; unmixing using; blind hyperspectral ... See more keywords