Results 161 to 170 of about 6,533 (191)

Robust Context Dependent Spectral Unmixing

2014 22nd International Conference on Pattern Recognition, 2014
A robust hyper spectral unmixing algorithm that finds multiple sets of end members is introduced. The algorithm, called Robust Context Dependent Spectral Unmixing (RCDSU), combines the advantages of context dependent unmixing and robust clustering. RCDSU adapts the unmixing to different regions, or contexts, of the spectral space. It combines fuzzy and
Hamdi Jenzri, Hichem Frigui, Paul D. Gader   +2 more
openaire   +1 more source

Variational methods for spectral unmixing of hyperspectral unmixing

2011
International ...
Eches, Olivier, Dobigeon, Nicolas, Tourneret, Jean-Yves, Snoussi, Hichem   +3 more
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Spectral unmixing using linear unmixing under spatial autocorrelation constraints

2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
This paper presents a spectral unmixing approach that is implemented using linear unminxing method by a genetic algorithm. The unmixing is constrained not only by the negativity and sum-to-one of the abundances of endmembers at each pixel but also by the spatial autocorrelation of their abundances among eight neighbor pixels.
Xianfeng Song, Xiaoguang Jiang, Xiaoping Rui   +2 more
openaire   +1 more source

Particle filter for spectral unmixing

2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control, 2011
The paper addresses the problem of identification of fluorescent molecules, or fluorophores, in biological samples obtained from time-resolved fluorescence microscopy. The contribution of this work is an algorithm, based on particle filter optimization, that solves the spectral unmixing problem for more than two fluorophores even when they present ...
Omar Gutierrez-Navarro, Edgar R. Arce-Santana, Daniel U. Campos-Delgado, Martin O. Mendez, Javier A. Jo   +4 more
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Local spectral unmixing for target detection

2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2016
Remotely sensed hyperspectral imagery provides, at each pixel, a radiance spectrum with up to hundreds of distinct wavelength channels. This high-dimensional spectral information allows for pixel-level material discrimination, including applications to remotely detecting the presence of particular materials of interest within a scene.
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Feature Extraction by Linear Spectral Unmixing

2004
Linear Spectral Unmixing (LSU) has been proposed for the analysis of hyperspectral images, to compute the fractional contribution of the detected endmembers to each pixel in the image. In this paper we propose that the fractional abundance coefficients to be used as features for the supervised classification of the pixels. Thus we compare them with two
Manuel Graña, Alicia D'Anjou
openaire   +1 more source

Variability of the endmembers in spectral unmixing

2019
Spectral unmixing is an inverse problem in hyperspectral imaging that aims at recovering the spectra of the pure constituents of an image (called endmembers), as well as at estimating the proportions of said materials in each pixel (called abundances).
Drumetz, Lucas, Chanussot, Jocelyn, Jutten, Christian   +2 more
openaire   +3 more sources

Spectral Unmixing Technique of HSI

2015
Relative to the classification technique, the spectral unmixing (Keshava and Mustard in IEEE Trans Sig Process Mag 19:44–57, 2002) i.e., soft classification technique started late. Although the spectral resolution of the hyperspectral image has been improved greatly, the spatial resolution of the corresponding land object target of the pixel has been ...
Liguo Wang, Chunhui Zhao
openaire   +1 more source

Spectral Unmixing

2022
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Spatio-temporal spectral unmixing of time-series images

Remote Sensing of Environment, 2021
Qunming Wang, Xiaohua Tong, Peter M Atkinson   +2 more
exaly