Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability [PDF]
Remote Sensing, 2020 Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set.
Tatsumi Uezato +2 more
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Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity, or Mismodeling Effects [PDF]
IEEE Transactions on Image Processing, 2016 This paper presents three hyperspectral mixture models jointly with Bayesian algorithms for supervised hyperspectral unmixing. Based on the residual component analysis model, the proposed general formulation assumes the linear model to be corrupted by an additive term whose expression can be adapted to account for nonlinearities (NL), endmember ...
Abderrahim Halimi +2 more
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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
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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.
Halimi, Abderrahim +3 more
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Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework [PDF]
CoRRThis 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 Hierarchical Sparsity Unmixing Method to Address Endmember Variability in Hyperspectral Image [PDF]
Remote Sensing, 2018 With a low spectral resolution hyperspectral sensor, the signal recorded from a given pixel against the complex background is a mixture of spectral contents. To improve the accuracy of classification and subpixel object detection, hyperspectral unmixing (
Jinlin Zou, Jinhui Lan, Yang Shao
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A multiple endmember mixing model to handle spectral variability [PDF]
, 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 ...
Uezato, Tatsumi +2 more
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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
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Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments [PDF]
Remote Sensing, 2016 Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel ...
Yingbin Deng, Changshan Wu
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Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands [PDF]
Remote Sensing, 2017 Variable environmental conditions cause different spectral responses of scene endmembers. Ignoring these variations affects the accuracy of fractional abundances obtained from linear spectral unmixing. On the other hand, the correlation between the bands
Omid Ghaffari +2 more
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