Cargando…
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion
Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster as...
Autores principales: | Meraner, Andrea, Ebel, Patrick, Zhu, Xiao Xiang, Schmitt, Michael |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386944/ https://www.ncbi.nlm.nih.gov/pubmed/32747852 http://dx.doi.org/10.1016/j.isprsjprs.2020.05.013 |
Ejemplares similares
-
Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
por: Huang, Jing-Shan, et al.
Publicado: (2021) -
Towards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery
por: Qiu, Chunping, et al.
Publicado: (2018) -
Efficient In‐Cloud Removal of Aerosols by Deep Convection
por: Yu, Pengfei, et al.
Publicado: (2019) -
Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
por: Han, Dong, et al.
Publicado: (2019) -
Assessment of Various Multimodal Fusion Approaches Using Synthetic Aperture Radar (SAR) and Electro-Optical (EO) Imagery for Vehicle Classification via Neural Networks †
por: Narayanan, Ram M., et al.
Publicado: (2023)