1,720,956 research outputs found
Contextual Classification of Polarimetric Sar Data Through a Complex-Valued Kernel and Global Energy Minimization
No full textThis paper addresses the challenges of supervised semantic segmentation using Polarimetric Synthetic Aperture Radar (PolSAR) data for land cover mapping. We extend previous approaches relying on spatial-contextual classifier based on Support Vector Machines (SVMs) and Markov Random Field (MRF) models. The kernel used in this work extends a previously presented complex formulation based on reproducing kernel Hilbert spaces (RKHS). In this paper, we present a symmetrized form of this complex kernel, integrating it with global energy minimization techniques, and show that it provides more accurate predictions. The proposed approach achieves competitive accuracy on benchmark datasets, comparable to those of deep learning algorithms. The method's advantage lies in its lower resource requirements, making it a promising alternative for PolSAR semantic segmentation
Manifold learning and deep generative networks for heterogeneous change detection from hyperspectral and synthetic aperture radar images
No full textUnsupervised change detection stands as a critical tool for damage assessment after a natural disaster. We emphasize heterogeneous change detection methods, which support the case of highly heterogeneous images at the two observation dates, providing greater flexibility than traditional homogeneous methods. This adaptability is vital for swift responses in the aftermath of natural disasters. In this framework, we address the challenging case of detecting changes between a hyperspectral and a synthetic aperture radar images. This case has intrinsic difficulties, namely the difference in the nature of the physical quantity measured, added to the great difference in dimensionality of the two imaging domains. To address these challenges, a novel method is proposed based on the integration of a manifold learning technique and deep learning networks trained to perform an image to image translation task. The method works in a fully unsupervised manner, further enforcing a fast implementation in real-world scenarios. From an application-oriented perspective, we focus on flooded-area mapping using the PRISMA and COSMO-SkyMed missions. The experimental validation on two datasets, a semi-simulated one and a real one associated with flooding, suggests that the proposed method allows for accurate detection of flooded areas and other ground changes
Heterogeneous change detection with PRISMA and COSMO-SkyMed Second Generation imagery for natural disaster management
No full textChange detection (CD) is among the most important tools in natural disaster monitoring. Special emphasis is on heterogeneous CD methods, which allow for a faster response. In this paper, we propose a novel heterogeneous CD method tailored at working with image domains of very different dimensionality, which allows for a greater applicational flexibility. The proposed method integrates deep image-to-image translation, spectral clustering concepts, and manifold learning, and works in a fully unsupervised manner, further enforcing a fast implementation in real-world scenarios. From an application-oriented perspective, the focus is on the recent PRISMA and COSMO-SkyMed missions of the Italian Space Agency
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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