Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework [PDF]
IEEE Transactions on Computational ImagingThis work proposes a variational inference (VI) framework for hyperspectral unmixing in the presence of endmember variability (HU-EV). An EV-accounted noisy linear mixture model (LMM) is considered, and the presence of outliers is also incorporated into the model. Following the marginalized maximum likelihood (MML) principle, a VI algorithmic structure
Yuening Li +3 more
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A multiple endmember mixing model to handle spectral variability in hyperspectral unmixing [PDF]
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018 This paper proposes a novel mixing model that incorporates spectral variability. The proposed approach relies on the following two ingredients: i) a mixed spectrum is modeled as a combination of a few endmember signatures which belong to some endmember bundles (referred to as classes), ii) sparsity is promoted for the selection of both endmember ...
Tatsumi Uezato +2 more
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Multilayer Simplex-Structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability [PDF]
2026 IEEE 14th Sensor Array and Multichannel Signal Processing Workshop (SAM)Given a hyperspectral image, the problem of hyperspectral unmixing (HU) is to identify the endmembers (or materials) and the abundance (or endmembers' contributions on pixels) that underlie the image. HU can be seen as a matrix factorization problem with a simplex structure in the abundance matrix factor.
Junbin Liu, Yuening Li, Wing‐Kin Ma
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Incorporating Endmember Variability into Linear Unmixing of Coarse Resolution Imagery: Mapping Large-Scale Impervious Surface Abundance Using a Hierarchically Object-Based Spectral Mixture Analysis [PDF]
Remote Sensing, 2015 As an important indicator of anthropogenic impacts on the Earth’s surface, it is of great necessity to accurately map large-scale urbanized areas for various science and policy applications.
Chengbin Deng
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Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery [PDF]
Remote Sensing, 2016 Spectral unmixing and sub-pixel mapping have been used to estimate the proportion and spatial distribution of the different land-cover classes in mixed pixels at a sub-pixel scale. In the past decades, several algorithms were proposed in both categories;
Xiong Xu +5 more
doaj +3 more sources
Improved Hyperspectral Unmixing with Endmember Variability Parametrized Using an Interpolated Scaling Tensor [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Endmember (EM) variability has an important impact on the performance of hyperspectral image (HI) analysis algorithms. Recently, extended linear mixing models have been proposed to account for EM variability in the spectral unmixing (SU) problem. The direct use of these models has led to severely ill-posed optimization problems.
Ricardo Augusto Borsoi +2 more
core +6 more sources
A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability [PDF]
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015 This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. Each image pixel is modeled by a linear combination of random endmembers to take into account endmember variability in the image.
Abderrahim Halimi +3 more
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Bayesian Subpixel Mapping of Hyperspectral Imagery via Discrete Endmember Variability Mixture Model and Markov Random Field [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Although Bayesian methods have been very effective for spatial–spectral analysis of hyperspectral imagery (HSI), they had not been fully explored for enhanced subpixel mapping (SPM) by simultaneously considering several key issues, i.e., endmember
Yujia Chen +6 more
doaj +2 more sources
Model-based analysis of the relationship between endmember variability and temporal unmixing performance [PDF]
GIScience & Remote SensingTemporal unmixing, an extension of traditional spectral unmixing in a multi-temporal context, leverages endmembers defined by their temporal signatures to decompose mixed pixel responses into fractional cover.
Da Zhang, Chen Shi
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Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
Applied Sciences, 2022 The existence of intra-class spectral variability caused by differential scene components and illumination conditions limits the improvement of endmember extraction accuracy, as most endmember extraction algorithms directly find pixels in the ...
Xiangyue Zhang, Yueming Wang, Tianru Xue
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