Institution
University of Extremadura
Education•Badajoz, Spain•
About: University of Extremadura is a education organization based out in Badajoz, Spain. It is known for research contribution in the topics: Population & Hyperspectral imaging. The organization has 7856 authors who have published 18299 publications receiving 396126 citations. The organization is also known as: Universidad de Extremadura.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The effect of polymer infiltration on the compressive strength of β-tricalcium phosphate (TCP) scaffolds fabricated by robocasting (direct write assembly) is analyzed and the implications for the mechanical optimization of scaffolds for bone tissue engineering applications are discussed.
176 citations
••
TL;DR: Thermal and ozone regenerations of granular activated carbons (GAC) used in the removal of phenol from aqueous solution have been studied and it was shown that spent carbons can recover most of their adsorption characteristics and specific surface areas when regenerated through a number of Adsorption-ozone regeneration cycles.
176 citations
••
TL;DR: Experiments indicate that the proposed strategy to adapt available EEAs to select multiple endmembers per scene component can significantly improve fractional abundance estimations by accounting for endmember variability in the original hyperspectral data.
Abstract: Spectral unmixing is an important task in hyperspectral data exploitation. It amounts to estimating the abundance of pure spectral constituents (endmembers) in each (possibly mixed) observation collected by the imaging instrument. In recent years, several endmember extraction algorithms (EEAs) have been proposed for automated endmember extraction from hyperspectral data sets. Traditionally, EEAs extract/select only one single standard endmember spectrum for each of the presented endmember classes or scene components. The use of fixed endmember spectra, however, is a simplification since in many cases the conditions of the scene components are spatially and temporally variable. As a result, variation in endmember spectral signatures is not always accounted for and, hence, spectral unmixing can lead to poor accuracy of the estimated endmember fractions. Here, we address this issue by developing a simple strategy to adapt available EEAs to select multiple endmembers (or bundles) per scene component. We run the EEAs in randomly selected subsets of the original hyperspectral image, and group the extracted samples of pure materials in a bundle using a clustering technique. The output is a spectral library of pure materials, extracted automatically from the input scene. The proposed technique is applied to several common EEAs and combined with an endmember variability reduction technique for unmixing purposes. Experiments with both simulated and real hyperspectral data sets indicate that the proposed strategy can significantly improve fractional abundance estimations by accounting for endmember variability in the original hyperspectral data.
176 citations
••
TL;DR: The results suggest that Asp646 is a molecular determinant of VR1 pore properties and imply that this residue may form a ring of negative charges that structures a high affinity binding site for cationic molecules at the extracellular entryway.
174 citations
••
TL;DR: In this paper, the influence of process variables such as the carbonisation temperature and the ZnCl 2 :CS ratio (impregnation ratio) on textural and chemical surface properties of the products obtained was studied.
173 citations
Authors
Showing all 8001 results
Name | H-index | Papers | Citations |
---|---|---|---|
Russel J. Reiter | 169 | 1646 | 121010 |
Donald G. Truhlar | 165 | 1518 | 157965 |
Manel Esteller | 146 | 713 | 96429 |
David J. Williams | 107 | 2060 | 62440 |
Keijo Häkkinen | 99 | 421 | 31355 |
Robert H. Anderson | 97 | 1237 | 41250 |
Leif Bertilsson | 87 | 321 | 23933 |
Mario F. Fraga | 84 | 267 | 32957 |
YangQuan Chen | 84 | 1048 | 36543 |
Antonio Plaza | 79 | 631 | 29775 |
Robert D. Gibbons | 75 | 349 | 26330 |
Jocelyn Chanussot | 73 | 614 | 27949 |
Naresh Magan | 72 | 400 | 17511 |
Luis Puelles | 71 | 269 | 19858 |
Jun Li | 70 | 799 | 19510 |