University of Extremadura

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.

Region-Based Spatial Preprocessing for Endmember Extraction and Spectral Unmixing

Abstract: Linear spectral unmixing is an important task in remotely sensed hyperspectral data exploitation. This approach first identifies a collection of spectrally pure constituent spectra, called endmembers, and then expresses the measured spectrum of each mixed pixel as a combination of endmembers weighted by fractions or abundances that indicate the proportion of each endmember in the pixel. Over the last decade, several algorithms have been developed for automatic extraction of spectral endmembers from hyperspectral data, with many of them relying exclusively on the spectral information. In this letter, we develop a novel unsupervised spatial preprocessing (SPP) module which adopts a region-based approach for the characterization of each endmember class prior to endmember identification using spectral information. The proposed approach can be combined with any spectral-based endmember extraction technique. Our method is validated using both synthetic scenes constructed using fractals and a real hyperspectral data set collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite Mining District in Nevada and further compared with previous efforts in the same direction such as the spatial-spectral endmember extraction, automatic morphological endmember extraction, or SPP methods.

Disposition of fluvoxamine in humans is determined by the polymorphic CYP2D6 and also by the CYP1A2 activity.

Abstract: Background Fluvoxamine is a selective serotonin reuptake inhibitor used widely in the treatment of depression and other psychiatric diseases, but little is known about the specific isozymes involved in its metabolism. This study investigated the relationship between fluvoxamine disposition and the polymorphic CYP2D6 and the polycyclic aromatic hydrocarbon (as contained in cigarette smoke) inducible CYP1A2. Methods Fluvoxamine (50 mg orally) was given to 10 extensive metabolizers and four poor metabolizers of debrisoquin, and concentrations were assessed in plasma by high performance liquid chromatography. Five of the extensive metabolizers and one of the poor metabolizers were smokers of more than 10 cigarettes per day. The CYP1A2 activity was determined by means of a urinary caffeine test. Results Compared with nonsmoking extensive metabolizers, nonsmoking poor metabolizers had a statistically significant (p = 0.02, Mann-Whitney Utest) about twofold higher maximum plasma concentration, longer half-life, and fivefold lower oral clearance of fluvoxamine. The oral clearance of fluvoxamine correlated to the CYP1A2 index in the 14 subjects (rs = 0.58; p < 0.05; Spearman rank correlation). Conclusion The disposition of fluvoxamine in humans is associated with the polymorphic CYP2D6 activity, but CYP1A2 also seems to be involved. Clinical Pharmacology & Therapeutics (1996) 60, 183–190; doi:

Spectral–Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization

Abstract: Remotely sensed hyperspectral image classification is a very challenging task. This is due to many different aspects, such as the presence of mixed pixels in the data or the limited information available a priori. This has fostered the need to develop techniques able to exploit the rich spatial and spectral information present in the scenes while, at the same time, dealing with mixed pixels and limited training samples. In this paper, we present a new spectral–spatial classifier for hyperspectral data that specifically addresses the issue of mixed pixel characterization. In our presented approach, the spectral information is characterized both locally and globally, which represents an innovation with regard to previous approaches for probabilistic classification of hyperspectral data. Specifically, we use a subspace-based multinomial logistic regression method for learning the posterior probabilities and a pixel-based probabilistic support vector machine classifier as an indicator to locally determine the number of mixed components that participate in each pixel. The information provided by local and global probabilities is then fused and interpreted in order to characterize mixed pixels. Finally, spatial information is characterized by including a Markov random field (MRF) regularizer. Our experimental results, conducted using both synthetic and real hyperspectral images, indicate that the proposed classifier leads to state-of-the-art performance when compared with other approaches, particularly in scenarios in which very limited training samples are available.