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
••
Mohammad H. Forouzanfar1, Lily Alexander1, H. Ross Anderson2, Victoria F Bachman1 +718 more•Institutions (295)
TL;DR: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) as mentioned in this paper provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution.
1,656 citations
••
Christopher J L Murray1, Ryan M Barber, Kyle J Foreman2, Ayse Abbasoglu Ozgoren +608 more•Institutions (251)
TL;DR: Patterns of the epidemiological transition with a composite indicator of sociodemographic status, which was constructed from income per person, average years of schooling after age 15 years, and the total fertility rate and mean age of the population, were quantified.
1,609 citations
••
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
••
Nicholas J Kassebaum1, Megha Arora1, Ryan M Barber1, Zulfiqar A Bhutta2 +679 more•Institutions (268)
TL;DR: In this paper, the authors used the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015.
1,533 citations
••
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 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 |