DO WE STILL NEED IMAGENET PRE-TRAINING IN REMOTE SENSING SCENE CLASSIFICATION?

10 citations as recorded by crossref.

1. A Robust Feature Downsampling Module for Remote-Sensing Visual Tasks W. Lu et al. https://doi.org/10.1109/TGRS.2023.3282048
2. Semantic-Aware Dense Representation Learning for Remote Sensing Image Change Detection H. Chen et al. https://doi.org/10.1109/TGRS.2022.3203769
3. A Systematic Study on Pretraining Strategies for Low-Label Remote Sensing Image Semantic Segmentation P. Liu et al. https://doi.org/10.3390/s26041385
4. Heterogeneous Prototype Distillation With Support-Query Correlative Guidance for Few-Shot Remote Sensing Scene Classification Y. Zhuang et al. https://doi.org/10.1109/TGRS.2024.3409637
5. In-Domain Supervised and Contrastive Self-Supervised Representation Learning for Dense Prediction Problems in Remote Sensing Imageries A. Ghanbarzadeh & H. Soleimani https://doi.org/10.1109/ACCESS.2024.3510779
6. Multi-level feature fusion networks for smoke recognition in remote sensing imagery Y. Wang et al. https://doi.org/10.1016/j.neunet.2024.107112
7. A Comparison of Machine Learning and Deep Learning Architectures with Explainable AI for Mapping Land Cover Using Sentinel-2 Satellite Data M. Younas et al. https://doi.org/10.3390/geomatics6050111
8. Enhancing People Localization in Drone Imagery for Better Crowd Management by Utilizing Every Pixel in High-Resolution Images B. Ptak & M. Kraft https://doi.org/10.1109/ACCESS.2026.3700840
9. Generic Knowledge Boosted Pretraining for Remote Sensing Images Z. Huang et al. https://doi.org/10.1109/TGRS.2024.3354031
10. Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis A. Thapa et al. https://doi.org/10.3390/rs15194804