Results 11 to 20 of about 1,373 (228)
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.
Halimi, Abderrahim +3 more
core +9 more sources
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
core +3 more sources
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 ...
Halimi, Abderrahim +4 more
core +7 more sources
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
doaj +2 more sources
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
doaj +2 more sources
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
openaire +3 more sources
Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery
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 +2 more sources
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
openaire +5 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.
Halimi, Abderrahim +3 more
openaire +5 more sources
Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework
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
openaire +3 more sources