Listar INIT_Articles por autoría "f0425174-76b6-4ce5-accf-5e4f2bba8109"
Mostrando ítems 1-20 de 23
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A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution
Haut, Juan M.; Fernandez-Beltran, Ruben; Paoletti, Mercedes Eugenia; Plaza, Javier; Plaza, Antonio; Pla, Filiberto 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 ... -
Capsule Networks for Hyperspectral Image Classification
Paoletti, Mercedes Eugenia; Haut, Juan M.; Fernandez-Beltran, Ruben; Plaza, Javier; Plaza, Antonio; Li, Jun; Pla, Filiberto 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 ... -
Deep Learning-Based Building Footprint Extraction With Missing Annotations
kang, jian; Fernandez-Beltran, Ruben; Sun, Xian; Ni, Jingen; Plaza, Antonio 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 ... -
Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization
kang, jian; Fernandez-Beltran, Ruben; Zhen, Ye; Tong, Xiaohua; Ghamisi, Pedram; Plaza, Antonio 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 ... -
Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification
Paoletti, Mercedes Eugenia; Haut, Juan M.; Fernandez-Beltran, Ruben; Plaza, Javier; Plaza, Antonio; Pla, Filiberto 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 ... -
Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast
kang, jian; Fernandez-Beltran, Ruben; Duan, Puhong; Liu, Sicong; Plaza, Antonio 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) ... -
Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor Moments
Fernandez-Beltran, Ruben; Pla, Filiberto; Plaza, Antonio 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 ... -
Graph Relation Network: Modeling Relations Between Scenes for Multilabel Remote-Sensing Image Classification and Retrieval
kang, jian; Fernandez-Beltran, Ruben; Danfeng, Hong; Chanussot, Jocelyn; Plaza, Antonio 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 ... -
High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
kang, jian; Fernandez-Beltran, Ruben; Ye, Zhen; Tong, Xiaohua; Ghamisi, Pedram; Plaza, Antonio 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 ... -
Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis
Fernandez-Beltran, Ruben; Plaza, Antonio; Plaza, Javier; Pla, Filiberto 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 ... -
Intersensor Remote Sensing Image Registration Using Multispectral Semantic Embeddings
Fernandez-Beltran, Ruben; Pla, Filiberto; Plaza, Antonio IEEE (2019-04)This letter presents a novel intersensor registration framework specially designed to register Sentinel-3 (S3) operational data using the Sentinel-2 (S2) instrument as a reference. The substantially higher resolution of ... -
Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification
Haut, Juan M.; Bernabé, Sergio; Paoletti, Mercedes Eugenia; Fernandez-Beltran, Ruben; Plaza, Antonio; Plaza, Javier IEEE (2018-11)Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models typically ... -
Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion
Fernandez-Beltran, Ruben; Haut, Juan M.; Paoletti, Mercedes Eugenia; Plaza, Javier; Plaza, Antonio; Pla, Filiberto IEEE (2018-09)Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of ... -
Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images With Label Noise
kang, jian; Fernandez-Beltran, Ruben; Kang, Xudong; Ni, Jingen; Plaza, Antonio IEEE (2021-02-02)Recently, many deep learning-based methods have been developed for solving remote sensing (RS) scene classification or retrieval tasks. Most of the adopted loss functions for training these models require accurate annotations. ... -
PiCoCo: Pixelwise Contrast and Consistency Learning for Semisupervised Building Footprint Segmentation
kang, jian; Wang, Zhirui; Zhu, Ruoxin; Sun, Xian; Fernandez-Beltran, Ruben; Plaza, Antonio IEEE (2021-10-11)Building footprint segmentation from high-resolution remote sensing (RS) images plays a vital role in urban planning, disaster response, and population density estimation. Convolutional neural networks (CNNs) have been ... -
Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis
Fernandez-Beltran, Ruben; Haut, Juan M.; Paoletti, Mercedes Eugenia; Plaza, Javier; Plaza, Antonio; Pla, Filiberto IEEE (2018-11)The generative semantic nature of probabilistic topic models has recently shown encouraging results within the remote sensing image fusion field when conducting land cover categorization. However, standard topic models ... -
Remote Sensing Image Superresolution Using Deep Residual Channel Attention
Haut, Juan M.; Fernandez-Beltran, Ruben; Paoletti, Mercedes Eugenia; Plaza, Javier; Plaza, Antonio IEEE (2019-07-23)The current trend in remote sensing image superresolution (SR) is to use supervised deep learning models to effectively enhance the spatial resolution of airborne and satellite-based optical imagery. Nonetheless, the ... -
Remote Sensing Single-Image Superresolution Based on a Deep Compendium Model
Haut, Juan M.; Paoletti, Mercedes Eugenia; Fernandez-Beltran, Ruben; Plaza, Javier; Plaza, Antonio; Li, Jun IEEE (2019-03)This letter introduces a novel remote sensing single-image superresolution (SR) architecture based on a deep efficient compendium model. The current deep learning-based SR trend stands for using deeper networks to improve ... -
Robust Normalized Softmax Loss for Deep Metric Learning-Based Characterization of Remote Sensing Images With Label Noise
kang, jian; Fernandez-Beltran, Ruben; Duan, Puhong; Kang, Xudong; Plaza, Antonio Institute of Electrical and Electronics Engineers (2020-12-16)Most deep metric learning-based image characterization methods exploit supervised information to model the semantic relations among the remote sensing (RS) scenes. Nonetheless, the unprecedented availability of large-scale ... -
Rotation-Invariant Deep Embedding for Remote Sensing Images
kang, jian; Fernandez-Beltran, Ruben; Wang, Zhirui; Sun, Xian; Ni, Jingen; Plaza, Antonio Institute of Electrical and Electronics Engineers (2021-06-28)Endowing convolutional neural networks (CNNs) with the rotation-invariant capability is important for characterizing the semantic contents of remote sensing (RS) images since they do not have typical orientations. Most of ...