REMOTE SENSING IMAGE CLASSIFICATION WITH THE SEN12MS DATASET

14 citations as recorded by crossref.

1. Q-SupCon: Quantum-Enhanced Supervised Contrastive Learning Architecture within the Representation Learning Framework A. Don & I. Khalil 10.1145/3660647
2. Unlocking the capabilities of explainable few-shot learning in remote sensing G. Lee et al. 10.1007/s10462-024-10803-5
3. There Are No Data Like More Data: Datasets for deep learning in Earth observation M. Schmitt et al. 10.1109/MGRS.2023.3293459
4. Deep Image Prior Amplitude SAR Image Anonymization E. Cannas et al. 10.3390/rs15153750
5. Learning From Self-Supervised Features for Hashing-Based Remote Sensing Image Retrieval J. Tang et al. 10.1109/LGRS.2023.3263498
6. Benchmarking and scaling of deep learning models for land cover image classification I. Papoutsis et al. 10.1016/j.isprsjprs.2022.11.012
7. Land Cover Mapping with Convolutional Neural Networks Using Sentinel-2 Images: Case Study of Rome G. Cecili et al. 10.3390/land12040879
8. Land Consumption Mapping with Convolutional Neural Network: Case Study in Italy G. Cecili et al. 10.3390/land11111919
9. Explaining the Effects of Clouds on Remote Sensing Scene Classification J. Gawlikowski et al. 10.1109/JSTARS.2022.3221788
10. SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal P. Ebel et al. 10.1109/TGRS.2022.3146246
11. 3DMAE: Joint SAR and Optical Representation Learning With Vertical Masking L. Zhang et al. 10.1109/LGRS.2023.3316193
12. Multitarget Domain Adaptation for Remote Sensing Classification Using Graph Neural Network S. Saha et al. 10.1109/LGRS.2022.3149950
13. Multi-Label Remote Sensing Image Land Cover Classification Based on a Multi-Dimensional Attention Mechanism H. You et al. 10.3390/rs15204979
14. Meta-learning to address diverse Earth observation problems across resolutions M. Rußwurm et al. 10.1038/s43247-023-01146-0