A Geographic Information-Assisted Temporal Mixture Analysis for Addressing the Issue of Endmember Class and Endmember Spectra Variability [PDF]
Sensors, 2017 Spectral mixture analysis (SMA) is a common approach for parameterizing biophysical fractions of urban environment and widely applied in many fields. For successful SMA, the selection of endmember class and corresponding spectra has been assumed as the ...
Wenliang Li, Changshan Wu
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An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing [PDF]
Remote Sensing, 2021 With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing.
Chuanlong Ye +5 more
doaj +5 more sources
Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability [PDF]
IEEE Transactions on Image Processing, 2015 This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to take into account their variability in the image.
Jean-Yves Tourneret +2 more
exaly +10 more sources
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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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
doaj +7 more sources
Hyperspectral unmixing accounting for spatial correlations and endmember variability [PDF]
2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015 This paper presents an unsupervised Bayesian algorithm for hyper-spectral image unmixing accounting for endmember variability. This variability is obtained by assuming that each pixel is a linear combination of random endmembers weighted by their corresponding abundances.
Abderrahim Halimi +3 more
core +12 more sources
Hyperspectral Unmixing With Endmember Variability via Alternating Angle Minimization [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2016 In hyperspectral unmixing applications, one typically assumes that a single spectrum exists for every endmember. In many scenarios, this is not the case, and one requires a set or a distribution of spectra to represent an endmember or class. This inherent spectral variability can pose severe difficulties in classical unmixing approaches. In this paper,
Paul Gader, Alina Zare, Rob Heylen
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Unmixing Multitemporal Hyperspectral Images Accounting For Endmember Variability [PDF]
2015 23rd European Signal Processing Conference (EUSIPCO), 2015 Publication in the conference proceedings of EUSIPCO, Nice, France ...
Nicolas Dobigeon B +4 more
core +11 more sources
Generalized Linear Mixing Model Accounting for Endmember Variability [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images. Recently, the extended linear mixing model (ELMM) has been proposed as a modification of the linear mixing model (LMM) to consider endmember variability effects resulting mainly from ...
Ricardo Augusto Borsoi +2 more
exaly +7 more sources
A Gaussian Mixture Model Representation of Endmember Variability in Hyperspectral Unmixing [PDF]
IEEE Transactions on Image Processing, 2018 Hyperspectral unmixing while considering endmember variability is usually performed by the normal compositional model (NCM), where the endmembers for each pixel are assumed to be sampled from unimodal Gaussian distributions. However, in real applications, the distribution of a material is often not Gaussian.
Anand Rangarajan +2 more
exaly +7 more sources