SEMANTIC SEGMENTATION OF BRAZILIAN SAVANNA VEGETATION USING HIGH SPATIAL RESOLUTION SATELLITE DATA AND U-NET

6 citations as recorded by crossref.

1. Integrating Convolutional Neural Network and Multiresolution Segmentation for Land Cover and Land Use Mapping Using Satellite Imagery S. Atik & C. Ipbuker https://doi.org/10.3390/app11125551
2. Semantic segmentation of clouds in satellite images based on U-Net++ architecture and attention mechanism P. Kaur Buttar & M. Sachan https://doi.org/10.1016/j.eswa.2022.118380
3. Multimodal and Multitemporal Land Use/Land Cover Semantic Segmentation on Sentinel-1 and Sentinel-2 Imagery: An Application on a MultiSenGE Dataset R. Wenger et al. https://doi.org/10.3390/rs15010151
4. Mapping the Brazilian savanna’s natural vegetation: A SAR-optical uncertainty-aware deep learning approach P. Silva Filho et al. https://doi.org/10.1016/j.isprsjprs.2024.09.019
5. Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery J. Solórzano et al. https://doi.org/10.3390/rs13183600
6. DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin E. Cherif et al. https://doi.org/10.3390/rs14195000