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.

Immunocompetence and condition-dependent sexual advertisement in male house sparrows (Passer domesticus)

Abstract: Summary1. We tested the condition-dependent hypothesis of sexual advertisement in housesparrows (Passer domesticus). Male house sparrows have a bib of black featherswhich serves as both a badge of social status and as a cue for female choice. Wemanipulated environmental conditions during the premoult period of juvenilehouse sparrows kept in outdoor aviaries. Birds were assigned to two treatments dif-fering in the amount of dietary proteins, which are known to a•ect the expressionof immune response in birds. We tested whether birds in the protein-rich grouphad better immune responses and developed larger bibs than birds reared on a pro-tein-poor diet. We also checked whether immune response was a predictor of survi-val and parasite resistance.2. Individuals with higher cellular immune response at capture had greater prob-ability to survive during the 3months of the experiment, and they had a higherprobability to recover from infection with Haemoproteus sp. (a blood parasite).Conversely, birds with high immunoglobulin concentrations at capture had ahigher probability of mortality.3. Birds on the protein-rich diet had a higher cellular immune response comparedto birds in the protein-poor treatment. Humoral immune response showed theopposite pattern, being higher for birds in the protein-poor treatment. We did notfind any e•ect of food quality on the development of the badge, assessed as the sizeof the trait and its colour properties.4. In conclusion, our results support the view that immune defences are importantfor survival and parasite resistance in natural populations, and that they might becostly to produce. On the other hand, we did not find support for the condition-dependent hypothesis of sexual advertisement, suggesting that the badge may notbe a costly trait to produce. However, badge size could reflect other aspects of con-dition. The kind of pigments involved in colour signals may be the key factor deter-mining the production costs of such traits.Key-words: immune response, immunoglobulins, parasite resistance, sexual signals,survival, T-cell response.Journal of Animal Ecology (1999) 68, 1225–1234Introduction

Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach

Abstract: Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques have been generally more popular, despite the fact that it is difficult to collect labeled samples in real scenarios. In particular, deep neural networks, such as convolutional neural networks (CNNs), have recently shown a great potential to yield high performance in the hyperspectral image classification. However, these techniques require sufficient labeled samples in order to perform properly and generalize well. Obtaining labeled data is expensive and time consuming, and the high dimensionality of hyperspectral data makes it difficult to design classifiers based on limited samples (for instance, CNNs overfit quickly with small training sets). Active learning (AL) can deal with this problem by training the model with a small set of labeled samples that is reinforced by the acquisition of new unlabeled samples. In this paper, we develop a new AL-guided classification model that exploits both the spectral information and the spatial-contextual information in the hyperspectral data. The proposed model makes use of recently developed Bayesian CNNs. Our newly developed technique provides robust classification results when compared with other state-of-the-art techniques for hyperspectral image classification.

Spatial Preprocessing for Endmember Extraction

Abstract: Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a remotely sensed hyperspectral scene. These pure signatures are then used to decompose the scene into abundance fractions by means of a spectral unmixing algorithm. Most techniques available in the endmember extraction literature rely on exploiting the spectral properties of the data alone. As a result, the search for endmembers in a scene is conducted by treating the data as a collection of spectral measurements with no spatial arrangement. In this paper, we propose a novel strategy to incorporate spatial information into the traditional spectral-based endmember search process. Specifically, we propose to estimate, for each pixel vector, a scalar spatially derived factor that relates to the spectral similarity of pixels lying within a certain spatial neighborhood. This scalar value is then used to weigh the importance of the spectral information associated to each pixel in terms of its spatial context. Two key aspects of the proposed methodology are given as follows: 1) No modification of existing image spectral-based endmember extraction methods is necessary in order to apply the proposed approach. 2) The proposed preprocessing method enhances the search for image spectral endmembers in spatially homogeneous areas. Our experimental results, which were obtained using both synthetic and real hyperspectral data sets, indicate that the spectral endmembers obtained after spatial preprocessing can be used to accurately model the original hyperspectral scene using a linear mixture model. The proposed approach is suitable for jointly combining spectral and spatial information when searching for image-derived endmembers in highly representative hyperspectral image data sets.

Monthly Electric Energy Demand Forecasting Based on Trend Extraction

Abstract: Medium-term electric energy demand forecasting is an essential tool for power system planning and operation, mainly in those countries whose power systems operate in a deregulated environment. This paper proposes a novel approach to monthly electric energy demand time series forecasting, in which it is split into two new series: the trend and the fluctuation around it. Then two neural networks are trained to forecast them separately. These predictions are added up to obtain an overall forecasting. Several methods have been tested to find out which of them provides the best performance in the trend extraction. The proposed technique has been applied to the Spanish peninsular monthly electric consumption. The results obtained are better than those reached when only one neural network was used to forecast the original consumption series and also than those obtained with the ARIMA method