This post covers the eighteenth lecture in the course: “Remote Sensing.”
This lecture will cover the application of deep learning methods to remote sensing (satelite imagery and other remotely-collected data).
References Cited in Lecture 18: Remote Sensing
Aleissaee et al. Transformers in Remote Sensing: A Survey, arXiv:2209.01206 [cs.CV] https://arxiv.org/abs/2209.01206
D. Wang, J. Zhang, B. Du, G.-S. Xia, D. Tao, “An Empirical Study of Remote Sensing Pretraining”, IEEE Trans. on Geoscience and Remote Sensing (TGRS), 2022 https://arxiv.org/abs/2204.02825
Gao, Yuan, Xiaojuan Sun, and Chao Liu. “A General Self-Supervised Framework for Remote Sensing Image Classification.” Remote Sensing 14.19 (2022): 4824.
Fuller, Anthony, Koreen Millard, and James R. Green. “SatViT: Pretraining Transformers for Earth Observation.” IEEE Geoscience and Remote Sensing Letters 19 (2022): 1-5.
Fuller, Anthony, Koreen Millard, and James R. Green. “Transfer Learning with Pretrained Remote Sensing Transformers.” arXiv preprint arXiv:2209.14969 (2022). (SatViT-V2)
Zhang, Tong, et al. “Consecutive Pre-Training: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain” Remote Sensing 14.22 (2022): 5675.
Bandara, Wele Gedara Chaminda, and Vishal M. Patel. “A transformer-based siamese network for change detection.” IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. https://arxiv.org/abs/2201.01293
Alosaimi, Najd, et al. “Self-supervised learning for remote sensing scene classification under the few shot scenario.” Scientific Reports 13.1 (2023): 433.
Repos and Data Sources
Google Earth Engine – API for downloading high-quality satelite images, free student version
- LandSat 8 (Under “Earth”)
- GIBS (Higher Quality, harder to work with)
- EarthData (Mostly for application development, but can be used for downloads too)
Image Source: www.cnn.com