MERCEDES EUGENIA
PAOLETTI ÁVILA
Profesora ayudante doctora
Rubén
Fernández Beltrán
Publicaciones en las que colabora con Rubén Fernández Beltrán (11)
2019
-
Capsule Networks for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, Núm. 4, pp. 2145-2160
-
Deep pyramidal residual networks for spectral-spatial hyperspectral image classification
IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, Núm. 2, pp. 740-754
-
GPU Parallel Implementation of Dual-Depth Sparse Probabilistic Latent Semantic Analysis for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 12, Núm. 9, pp. 3156-3167
-
Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, Vol. 16, Núm. 5, pp. 776-780
-
Open Multi-Processing Acceleration for Unsupervised Land Cover Categorization Using Probabilistic Latent Semantic Analysis
International Geoscience and Remote Sensing Symposium (IGARSS)
-
Remote Sensing Image Superresolution Using Deep Residual Channel Attention
IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, Núm. 11, pp. 9277-9289
-
Remote sensing single-image superresolution based on a deep compendium model
IEEE Geoscience and Remote Sensing Letters, Vol. 16, Núm. 9, pp. 1432-1436
2018
-
A new deep generative network for unsupervised remote sensing single-image super-resolution
IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, Núm. 11, pp. 6792-6810
-
Inter-sensor regression analysis for operational sentinel-2 and sentinel-3 data products
International Geoscience and Remote Sensing Symposium (IGARSS)
-
Multimodal probabilistic latent semantic analysis for Sentinel-1 and Sentinel-2 image fusion
IEEE Geoscience and Remote Sensing Letters, Vol. 15, Núm. 9, pp. 1347-1351
-
Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11, Núm. 12, pp. 4982-4993