Listar por autoría "16bb935f-0301-4d5d-9881-fe6f7e4557b9"
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A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution
IEEE (2018-11)Super-resolution (SR) brings an excellent opportunity to improve a wide range of different remote sensing applications. SR techniques are concerned about increasing the image resolution while providing finer spatial details ... -
Arm Swing Asymmetry Measurement from 2D Gait Videos
MDPI (2021-10-25)Arm swing during gait has been positively related to gait stability and gait efficiency, particularly in the presence of neurological disorders that affect locomotion. However, most gait studies have focused on lower ...
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Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images
IEEE (2022-08-15)Monitoring water bodies from remote sensing data is certainly an essential task to supervise the actual conditions of the available water resources for environment conservation, sustainable development, and many other ...
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Capsule Networks for Hyperspectral Image Classification
IEEE (2018-10)Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when ...
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DAT-CNN: Dual Attention Temporal CNN for Time-Resolving Sentinel-3 Vegetation Indices
IEEE (2022-03-22)The synergies between Sentinel-3 (S3) and the forthcoming fluorescence explorer (FLEX) mission bring us the opportunity of using S3 vegetation indices (VI) as proxies of the solar-induced chlorophyll fluorescence (SIF) ...
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Deep Hashing Based on Class-Discriminated Neighborhood Embedding
Institute of Electrical and Electronics Engineers (2020-09-30)Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS) owing to their prominent capabilities for capturing the semantics from complex RS scenes and generating the ...
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Deep Learning-Based Building Footprint Extraction With Missing Annotations
Institute of Electrical and Electronics Engineers (2021-04-21)Most state-of-the-art deep learning-based methods for extraction of building footprints are aimed at designing proper convolutional neural network (CNN) architectures or loss functions able to effectively predict building ...
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Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization
Institute of Electrical and Electronics Engineers (2020-05-12)With the development of convolutional neural networks (CNNs), the semantic understanding of remote sensing (RS) scenes has been significantly improved based on their prominent feature encoding capabilities. While many ...
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Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification
IEEE (2018-08)Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed ...
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Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast
IEEE (2020-07-14)Convolutional neural networks (CNNs) have achieved great success when characterizing remote sensing (RS) images. However, the lack of sufficient annotated data (together with the high complexity of the RS image domain) ...
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Desarrollo de productos avanzados para la misión SEOSAT/Ingenio
Asociación Española de Teledetección (2016)SEOSAT/Ingenio es la futura misión española de observación de la Tierra en el óptico en alta resolución espacial. Mientras que los productos de imagen a Nivel 1, radiancias geo-referenciadas a nivel de sensor, se encuentran ...
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Descriptores genéricos de bajo nivel en secuencias de vídeo
(2011)El presente trabajo consiste en explorar y desarrollar técnicas de extracción de características de bajo nivel en vídeos. Concretamente, a partir del estudio de los últimos avances y tendencias en la caracterización de ... -
Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor Moments
Institute of Electrical and Electronics Engineers (2020-01-13)This letter presents a novel hyperspectral endmember extraction approach that integrates a tensor-based decomposition scheme with a probabilistic framework in order to take advantage of both technologies when uncovering ...
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Fundamentals of automated human gesture recognition using 3D integral imaging: a tutorial
Optical Society of America (2020-12-31)Automated human gesture recognition is receiving significant research interest, with applications ranging from novel acquisition techniques to algorithms, data processing, and classification methodologies. This tutorial ...
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GPU Parallel Implementation of Dual-Depth Sparse Probabilistic Latent Semantic Analysis for Hyperspectral Unmixing
IEEE (2019-08-22)Hyperspectral unmixing (HU) is an important task for remotely sensed hyperspectral (HS) data exploitation. It comprises the identification of pure spectral signatures (endmembers) and their corresponding fractional abundances ...
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Graph Relation Network: Modeling Relations Between Scenes for Multilabel Remote-Sensing Image Classification and Retrieval
IEEE (2020-08-21)Due to the proliferation of large-scale remote-sensing (RS) archives with multiple annotations, multilabel RS scene classification and retrieval are becoming increasingly popular. Although some recent deep learning-based ...
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High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
MDPI (2020)Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic ...
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Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis
IEEE (2018-06)This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when ...
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Identification of Latent Topics in Patients Surviving COVID-19 in Mexico
IEEE (2023-01-11)With the outbreak of the SARS-CoV-2 o COVID-19 pandemic, multiple studies of risk factors and their influence on patient deaths have been developed. However, little attention is often paid to analyzing patients in risk ...
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Incremental probabilistic Latent Semantic Analysis for video retrieval
Elsevier (2015-06)Recent research trends in Content-based Video Retrieval have shown topic models as an effective tool to deal with the semantic gap challenge. In this scenario, this paper has a dual target: (1) it is aimed at studying ...
