1,721,247 research outputs found
Tree-structured Markov random field models for segmentation of noisy images with application to remote sensing (PhD Thesis)
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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
A CNN-based model for pansharpening of worldview-3 images
No full textFusing a multispectral image with a co-registered higher resolution single panchromatic band, provided by any multiresolution satellite systems, to rise the resolution of the former to that of the latter is known as pansharpening, and can be regarded as a guided super-resolution problem. Recently the use of convolutional neural networks (CNNs) has been extended to the pansharpening problem achieving state-of-the-art performance. Following this research line, the objective of this work was two-fold: provide a trained CNN model fitted to a specific sensor (WorldView-3) and explore a range of architectural configurations varied in both width and depth, seeking for the optimal one. Numerical and visual results show that the proposed solution compares favourably against reference methods
An Unsupervised CNN-Based Pansharpening Framework with Spectral-Spatial Fidelity Balance
No full textIn recent years, deep learning techniques for pansharpening multiresolution images have gained increasing interest. Due to the lack of ground truth data, most deep learning solutions rely on synthetic reduced-resolution data for supervised training. This approach has limitations due to the statistical mismatch between real full-resolution and synthetic reduced-resolution data, which affects the models’ generalization capacity. Consequently, there has been a shift towards unsupervised learning frameworks for pansharpening deep learning-based techniques. Unsupervised schemes require defining sophisticated loss functions with at least two components: one for spectral quality, ensuring consistency between the pansharpened image and the input multispectral component, and another for spatial quality, ensuring consistency between the output and the panchromatic input. Despite promising results, there has been limited investigation into the interaction and balance of these loss terms to ensure stability and accuracy. This work explores how unsupervised spatial and spectral consistency losses can be reliably combined preserving the outocome quality. By examining these interactions, we propose a general rule for balancing the two loss components to enhance the stability and performance of unsupervised pansharpening models. Experiments on three state-of-the-art algorithms using WorldView-3 images demonstrate that methods trained with the proposed framework achieve good performance in terms of visual quality and numerical indexes
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
Progettazione e utilizzo di un sistema sensorizzato a supporto del processo produttivo di pressocolata
Full text linkAppropriate 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
A detail-preserving cross-scale learning strategy for CNN-based pansharpening
No full textThe fusion of a single panchromatic (PAN) band with a lower resolution multispectral (MS) image to raise the MS resolution to that of the PAN is known as pansharpening. In the last years a paradigm shift from model-based to data-driven approaches, in particular making use of Convolutional Neural Networks (CNN), has been observed. Motivated by this research trend, in this work we introduce a cross-scale learning strategy for CNN pansharpening models. Early CNN approaches resort to a resolution downgrading process to produce suitable training samples. As a consequence, the actual performance at the target resolution of the models trained at a reduced scale is an open issue. To cope with this shortcoming we propose a more complex loss computation that involves simultaneously reduced and full resolution training samples. Our experiments show a clear image enhancement in the full-resolution framework, with a negligible loss in the reduced-resolution space
A Cross-Scale Loss for CNN-Based Pansharpening
No full textTo cope with the lack of input-output training samples, deep learning (DL) methods for pansharpening usually resort to Wald's protocol or other similar downscaling processes. By doing so, the scaled versions of the multispectral (MS) and panchromatic (PAN) components serve as input while the original MS plays as output during the training phase. As a side effect, the informational gap between reduced and full scales causes a mismatch between the training and test phases. In fact, DL methods typically provide a pretty good performance at reduced scale, with a good margin over traditional solutions that tends to vanish in the full-resolution framework. In this work, we propose a training framework that involves both the reduced and the full scale versions of the multiresolution image samples. This is achieved thanks to a suitably defined loss which comprises costs for both scales. Our numerical and visual experimental results confirm that the proposed approach provides an improved performance in the full-resolution case
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