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.

Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery

Abstract: In recent years, the support vector machines (SVMs) have been very successful in remote sensing image classification, particularly when dealing with high-dimensional data and limited training samples. Nevertheless, the vector-based feature alignment of the SVM can lead to an information loss in representation of hyperspectral images, which intrinsically have a tensor-based data structure. In this paper, a new multiclass support tensor machine (STM) is specifically developed for hyperspectral image classification. Our newly proposed STM processes the hyperspectral image as a data cube and then identifies the information classes in tensor space. The multiclass STM is developed from a set of binary STM classifiers using the one-against-one parallel strategy. As a part of our tensor-based processing chain, a multilinear principal component analysis (MPCA) is used for preprocessing, in order to reduce the tensorial data redundancy and, at the same time, preserve the tensorial structure information in sparse and high-order subspaces. As a result, the contributions of this work are twofold: a new multiclass STM model for hyperspectral image classification is developed, and a tensorial image interpretation framework is constructed, which provides a system consisting of tensor-based feature representation, feature extraction, and classification. Experiments with four hyperspectral data sets, covering agricultural and urban areas, are conducted to validate the effectiveness of the proposed framework. Our experimental results show that the proposed STM and MPCA-STM can achieve better results than traditional SVM-based classifiers.

Skip-Connected Covariance Network for Remote Sensing Scene Classification

Abstract: This paper proposes a novel end-to-end learning model, called skip-connected covariance (SCCov) network, for remote sensing scene classification (RSSC) The innovative contribution of this paper is to embed two novel modules into the traditional convolutional neural network (CNN) model, ie, skip connections and covariance pooling The advantages of newly developed SCCov are twofold First, by means of the skip connections, the multi-resolution feature maps produced by the CNN are combined together, which provides important benefits to address the presence of large-scale variance in RSSC data sets Second, by using covariance pooling, we can fully exploit the second-order information contained in such multi-resolution feature maps This allows the CNN to achieve more representative feature learning when dealing with RSSC problems Experimental results, conducted using three large-scale benchmark data sets, demonstrate that our newly proposed SCCov network exhibits very competitive or superior classification performance when compared with the current state-of-the-art RSSC techniques, using a much lower amount of parameters Specifically, our SCCov only needs 10% of the parameters used by its counterparts

Robocasting of 45S5 bioactive glass scaffolds for bone tissue engineering

Abstract: Three dimensional scaffolds with controlled pore architecture were prepared from 45S5 Bioglass ® powders by robocasting (direct-writing) using carboxymethyl cellulose (CMC) as the single processing additive. A comprehensive sintering study of the resulting structures was performed within the temperature range 500–1050 °C. Robocast scaffolds with interconnected porosities ranging from 60 to 80% were obtained for a fixed scaffold design. All scaffolds exhibited compressive strengths comparable to that of cancellous bone (2–13 MPa), including those sintered at temperatures below the crystallization temperature of 45S5 bioactive glass. These strength values are substantially higher than any previously reported data for 45S5 Bioglass ® scaffolds and imply that robocasting is the first technique which can be considered suitable for producing vitreous 45S5 scaffolds with a sufficient mechanical integrity for any practical application. Moreover, this process will enable the development of 45S5 Bioglass ® scaffolds with customized external geometry, and optimized pore architecture.