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Multi-temporal data augmentation for high frequency satellite imagery: a case study in Sentinel-1 and Sentinel-2 building and road segmentation
(ISPRS, 2022)
Contribución a congreso / Biltzarrerako ekarpena,
Semantic segmentation of remote sensing images has many practical applications such as urban planning or disaster assessment.
Deep learning-based approaches have shown their usefulness in automatically segmenting large ...
Super-resolution for Sentinel-2 images
(International Society for Photogrammetry and Remote Sensing, 2019)
info:eu-repo/semantics/conferenceObject,
Obtaining Sentinel-2 imagery of higher spatial resolution than the native bands while ensuring that output imagery preserves the original radiometry has become a key issue since the deployment of Sentinel-2 satellites. ...
Additional feature layers from ordered aggregations for deep neural networks
(IEEE, 2020)
info:eu-repo/semantics/conferenceObject,
In the last years we have seen huge advancements in the area of Machine Learning, specially with the use of Deep Neural Networks. One of the most relevant examples is in image classification, where convolutional neural ...
Learning super-resolution for Sentinel-2 images with real ground truth data from a reference satellite
(Copernicus, 2020)
info:eu-repo/semantics/conferenceObject,
Copernicus program via its Sentinel missions is making earth observation more accessible and affordable for everybody. Sentinel-2 images provide multi-spectral information every 5 days for each location. However, the maximum ...