WEAKLY SUPERVISED SEMANTIC SEGMENTATION OF SATELLITE IMAGES FOR LAND COVER MAPPING – CHALLENGES AND OPPORTUNITIES

29 citations as recorded by crossref.

1. A Weakly Supervised Semantic Segmentation Framework for Medium-Resolution Forest Classification With Noisy Labels and GF-1 WFV Images X. Peng et al. 10.1109/TGRS.2024.3404953
2. Land Use and Land Cover Mapping in the Era of Big Data C. Zhang & X. Li 10.3390/land11101692
3. The Outcome of the 2021 IEEE GRSS Data Fusion Contest - Track DSE: Detection of Settlements Without Electricity Y. Ma et al. 10.1109/JSTARS.2021.3130446
4. 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. 10.3390/rs14195000
5. Large-Scale Land Cover Mapping Framework Based on Prior Product Label Generation: A Case Study of Cambodia H. Zhu et al. 10.3390/rs16132443
6. Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba Q. Zhang et al. 10.3390/rs16193622
7. Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data G. Baier et al. 10.1109/TGRS.2021.3068532
8. SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal P. Ebel et al. 10.1109/TGRS.2022.3146246
9. A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability R. Qin & T. Liu 10.3390/rs14030646
10. Assessing the Relationship between Land Surface Temperature and Composition Elements of Urban Green Spaces during Heat Waves Episodes in Mediterranean Cities M. Delgado-Capel et al. 10.3390/f15030463
11. Self-Supervised SAR-Optical Data Fusion of Sentinel-1/-2 Images Y. Chen & L. Bruzzone 10.1109/TGRS.2021.3128072
12. High-Resolution Land-Cover Mapping Based on a Cross-Resolution Deep Learning Framework and Available Low-Resolution Labels X. Qi et al. 10.1109/JSTARS.2023.3342994
13. Deep Learning Methods for Semantic Segmentation in Remote Sensing with Small Data: A Survey A. Yu et al. 10.3390/rs15204987
14. Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications T. Hoeser et al. 10.3390/rs12183053
15. Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map R. Dong et al. 10.3390/rs12091418
16. AgMTR: Agent Mining Transformer for Few-Shot Segmentation in Remote Sensing H. Bi et al. 10.1007/s11263-024-02252-y
17. A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision N. Manakitsa et al. 10.3390/technologies12020015
18. Weakly Supervised Remote Sensing Image Semantic Segmentation With Pseudo-Label Noise Suppression X. Lu et al. 10.1109/TGRS.2024.3421890
19. One Model Is Enough: Toward Multiclass Weakly Supervised Remote Sensing Image Semantic Segmentation Z. Li et al. 10.1109/TGRS.2023.3290242
20. How Does Super-Resolution for Satellite Imagery Affect Different Types of Land Cover? Sentinel-2 Case A. Malczewska & M. Wielgosz 10.1109/JSTARS.2023.3328997
21. High-Resolution Land Cover Mapping Through Learning With Noise Correction R. Dong et al. 10.1109/TGRS.2021.3068280
22. A dual-branch weakly supervised learning based network for accurate mapping of woody vegetation from remote sensing images Y. Cheng et al. 10.1016/j.jag.2023.103499
23. A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks S. Illarionova et al. 10.3390/rs14225861
24. Global Land-Cover Mapping With Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest C. Robinson et al. 10.1109/JSTARS.2021.3063849
25. BGFNet: Semantic Segmentation Network Based on Boundary Guidance X. Sun et al. 10.1109/LGRS.2023.3333017
26. Superpixel Consistency Saliency Map Generation for Weakly Supervised Semantic Segmentation of Remote Sensing Images X. Zeng et al. 10.1109/TGRS.2023.3264232
27. Index Your Position: A Novel Self-Supervised Learning Method for Remote Sensing Images Semantic Segmentation D. Muhtar et al. 10.1109/TGRS.2022.3177770
28. On the Effectiveness of Weakly Supervised Semantic Segmentation for Building Extraction From High-Resolution Remote Sensing Imagery Z. Li et al. 10.1109/JSTARS.2021.3063788
29. Weakly Supervised Segmentation of Buildings in Digital Elevation Models A. Soliman et al. 10.1109/LGRS.2022.3177160