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Institution

University of Extremadura

EducationBadajoz, 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
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Journal ArticleDOI
TL;DR: In this article, a study of the steam gasification of Cynara cardunculus L. was carried out in order to characterise the gas phase with a view to its energy use, analysing the influence of water partial pressure, particle size, and temperature.

90 citations

Journal ArticleDOI
TL;DR: In this article, the effect of grain refinement in the sliding-wear resistance of α-Al 2 O 3 /β -Al 2 TiO 5 polycrystalline ceramics was studied.

90 citations

Journal ArticleDOI
TL;DR: In this article, a characterization and thermogravimetric study has been carried out on tomato residue and a kinetic model has been developed based on the degradation of hemicellulose, cellulose, lignin and oil that describes the pyrolysis of peel, seeds and peel and seeds residues.

90 citations

Journal ArticleDOI
TL;DR: It is shown that a well-defined crescent domain of high BMP activity located at the tip of the forming digits, which is termed the digit crescent (DC), directs incorporation and differentiation of the PZ mesenchymal cells into the digit aggregates.

90 citations

Journal ArticleDOI
TL;DR: The proposed sparse and low-rank technique for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers.
Abstract: The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available.

90 citations


Authors

Showing all 8001 results

NameH-indexPapersCitations
Russel J. Reiter1691646121010
Donald G. Truhlar1651518157965
Manel Esteller14671396429
David J. Williams107206062440
Keijo Häkkinen9942131355
Robert H. Anderson97123741250
Leif Bertilsson8732123933
Mario F. Fraga8426732957
YangQuan Chen84104836543
Antonio Plaza7963129775
Robert D. Gibbons7534926330
Jocelyn Chanussot7361427949
Naresh Magan7240017511
Luis Puelles7126919858
Jun Li7079919510
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202353
2022206
20211,260
20201,344
20191,230
20181,003