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Institution

Grenoble Institute of Technology

EducationGrenoble, France
About: Grenoble Institute of Technology is a education organization based out in Grenoble, France. It is known for research contribution in the topics: Hyperspectral imaging & Environmental science. The organization has 3427 authors who have published 5345 publications receiving 137158 citations. The organization is also known as: Grenoble INP.


Papers
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Journal ArticleDOI
TL;DR: This paper presents an overview of un Mixing methods from the time of Keshava and Mustard's unmixing tutorial to the present, including Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixed algorithms.
Abstract: Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

2,373 citations

Journal ArticleDOI
TL;DR: An overview of recent progress in the area of cellulose nanofibre-based nanocomposites is given in this article, with particular emphasis on applications, such as reinforced adhesives, to make optically transparent paper for electronic displays, to create DNA-hybrid materials, to generate hierarchical composites and for use in foams, aerogels and starch nanocom composites.
Abstract: This paper provides an overview of recent progress made in the area of cellulose nanofibre-based nanocomposites. An introduction into the methods used to isolate cellulose nanofibres (nanowhiskers, nanofibrils) is given, with details of their structure. Following this, the article is split into sections dealing with processing and characterisation of cellulose nanocomposites and new developments in the area, with particular emphasis on applications. The types of cellulose nanofibres covered are those extracted from plants by acid hydrolysis (nanowhiskers), mechanical treatment and those that occur naturally (tunicate nanowhiskers) or under culturing conditions (bacterial cellulose nanofibrils). Research highlighted in the article are the use of cellulose nanowhiskers for shape memory nanocomposites, analysis of the interfacial properties of cellulose nanowhisker and nanofibril-based composites using Raman spectroscopy, switchable interfaces that mimic sea cucumbers, polymerisation from the surface of cellulose nanowhiskers by atom transfer radical polymerisation and ring opening polymerisation, and methods to analyse the dispersion of nanowhiskers. The applications and new advances covered in this review are the use of cellulose nanofibres to reinforce adhesives, to make optically transparent paper for electronic displays, to create DNA-hybrid materials, to generate hierarchical composites and for use in foams, aerogels and starch nanocomposites and the use of all-cellulose nanocomposites for enhanced coupling between matrix and fibre. A comprehensive coverage of the literature is given and some suggestions on where the field is likely to advance in the future are discussed.

2,214 citations

Posted Content
TL;DR: An overview of unmixing methods from the time of Keshava and Mustard's tutorial as mentioned in this paper to the present can be found in Section 2.2.1].
Abstract: Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

1,808 citations

Journal ArticleDOI
TL;DR: A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing.
Abstract: Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

1,604 citations

Journal ArticleDOI
TL;DR: A seminal view on recent advances in techniques for hyperspectral image processing, focusing on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information.

1,481 citations


Authors

Showing all 3527 results

NameH-indexPapersCitations
J. F. Macías-Pérez13448694715
J-Y. Hostachy11971665686
Alain Dufresne11135845904
David Brown105125746827
Raphael Noel Tieulent8941724926
Antonio Plaza7963129775
G. Conesa Balbastre7620818800
Jocelyn Chanussot7361427949
Ekhard K. H. Salje7058119938
Richard Wilson7080921477
Jerome Bouvier7027813724
David Maurin6821517295
Alessandro Gandini6734819813
Matthieu Tristram6714317188
D. Santos6511315648
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023106
2022157
2021160
2020142
2019146
2018152