Results 31 to 40 of about 1,244,965 (221)
An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels
Remote Sensing, 2015 Endmember selection is the basis for sub-pixel land cover classifications using multiple endmember spectral mixture analysis (MESMA) that adopts variant endmember matrices for each pixel to mitigate errors caused by endmember variability in SMA.
Yuanliu Xu, Jiancheng Shi, Jinyang Du
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Variability of the endmembers in spectral unmixing: Recent advances [PDF]
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016 Endmember variability has been identified as one of the main limitations of the usual Linear Mixing Model, conventionally used to perform spectral unmixing of hyperspectral data. The topic is currently receiving a lot of attention from the community, and many new algorithms have recently been developed to model this variability and take it into account.
Lucas Drumetz +2 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHigh-precision extraction of alteration mineral information provides crucial guidance for hyperspectral remote sensing mineral exploration. Currently, widely used hyperspectral datasets (such as ZY-1 02D and GF-5) lack targeted design in spectral ...
Linpei Yang +3 more
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Spectral endmember variability on hyperspectral datasets of a martian meteorite — implications for planetary surfaces [PDF]
Icarus, 2021 Abstract Visible-near-infrared reflectance spectra reveal clues about the mineralogy and composition of planetary surfaces. In most cases, light interacts with a mixture of various components on planetary surfaces. While radiative transfer models have been developed to simulate mixture in particulate surfaces, the extent to which spectral features ...
Lu Pan +4 more
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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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Robust Supervised Method for Nonlinear Spectral Unmixing Accounting for Endmember Variability
IEEE Transactions on Geoscience and Remote Sensing, 2021 Due to the complex interaction of light with mixed materials, reflectance spectra are highly nonlinearly related to the pure material endmember spectra, making it hard to estimate the fractional abundances of the materials. Changing illumination conditions and cross-sensor situations cause spectral variability, further complicating the unmixing ...
Bikram Koirala +3 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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Sum-Product Unmixing for Hyperspectral Analysis With Endmember Variability
IEEE Geoscience and Remote Sensing Letters, 2018 Models of endmember variability capture the notion that multiple spectra may represent a single class or material, and while these models are physically realistic, they often give rise to excessive computational complexity during the spectral unmixing process.
Charan Puladas +2 more
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Endmember Variability in hyperspectral image unmixing
, 2016 Variabilité spectrale dans le démélange d'images hyperspectrales La finesse de la résolution spectrale des images hyperspectrales en télédétection permet une analyse précise de la scène observée, mais leur résolution spatiale est limitée, et un pixel acquis par le capteur est souvent un mélange des contributions de différents matériaux.
Lucas Drumetz
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Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability [PDF]
CoRR, 2018 In this paper, we model a pixel as a linear combination of endmembers sampled from probability distributions of Gaussian mixture models (GMM). The parameters of the GMM distributions are estimated using spectral libraries. Abundances are estimated based on the distribution parameters.
Yuan Zhou, Erin B. Wetherley, Paul Gader
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