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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
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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,
Alina Zare +2 more
exaly +4 more sources
A Gaussian Mixture Model Representation of Endmember Variability in Hyperspectral Unmixing
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, Yuan Zhou
exaly +5 more sources
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Endmember orthonormal mapping in hyperspectral mixture analysis to address endmember variability
Earth Science Informatics, 2016Spectral unmixing estimates the abundance of each endmember at every pixel of a hyperspectral image. Each material in traditional unmixing algorithms is represented through a constant spectral signature. However, endmember variability always exists due to environmental, atmospheric, and temporal conditions, which leads to poor accuracy of the estimated
Mohammad Mehdi Ebadzadeh +2 more
exaly +2 more sources
Endmember extraction analysis considering endmember variability
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013In recent years, several kinds of endmember extraction algorithms have been proposed from hyperspectral data set which extracts/selects one single standard endmember spectrum for each existing endmember class or scene component. In this article, endmember variability is considered to the mixture spectrum analysis by representing each endmember by a set
Liangpei Zhang, Mingming Xu
exaly +2 more sources
A Novel Spectral Unmixing Method Incorporating Spectral Variability Within Endmember Classes
IEEE Transactions on Geoscience and Remote Sensing, 2016Arman Melkumyan +2 more
exaly
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
doaj +7 more sources