Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/113966
Título: A dual network for super-resolution and semantic segmentation of sentinel-2 imagery
Autores/as: Abadal, Saüc
Salgueiro, Luis
Marcello Ruiz, Francisco Javier 
Vilaplana, Verónica
Clasificación UNESCO: 250404 Fotogrametría geodésica
250407 Geodesia por satélites
332401 Satélites artificiales
Palabras clave: Convolutional neural network
Deep learning
Semantic segmentation
Sentinel-2
Super-resolution
Fecha de publicación: 2021
Proyectos: PID2020-117142GB-I00
MCIN/AEI/10.13039/501100011033
Publicación seriada: Remote Sensing 
Resumen: There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input image, and the Semantic Segmentation Super-Resolution branch, that predicts a high-resolution segmentation map with a scaling factor of 2. We performed several experiments to find the best architecture, training and testing on a subset of the S2GLC 2017 dataset. We based our model on the DeepLabV3+ architecture, enhancing the model and achieving an improvement of 5% on IoU and almost 10% on the recall score. Furthermore, our qualitative results demonstrate the effectiveness and usefulness of the proposed approach.
URI: http://hdl.handle.net/10553/113966
ISSN: 2072-4292
DOI: 10.3390/rs13224547
Fuente: Remote Sensing [ISSN 2072-4292], v. 13(22), 4547, (Noviembre 2021)
Colección:Artículos
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