[38] S. Ren, D. Zhou, S. He, J. Feng, and X. Wang. Shunted self-attention via multi-scale token aggregation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10853–10862, 2022.
[39] A. Rosenfeld and M. Thurston. Edge and curve detection for visual scene analysis. IEEE Transactions on Computers, 100(5):562-569, 1971.
[40] B. Singh and L. S. Davis. An analysis of scale invariance in object detection snip. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3578-3587, 2018.
[41] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-9, 2015.
[42] M. Tang, K. Georgiou, H. Qi, C. Champion, and M. Bosch. Semantic segmentation in aerial imagery using multi-level contrastive learning with local consistency. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3798-3807, 2023.
[43] B. Tellman, J. A. Sullivan, and C. S. Doyle. Global flood observation with multiple satellites: Applications in rio salado (argentina) and the eastern nile basin. Global Drought and Flood: Observation, Modeling, and Prediction, pages 99–121, 2021.
[44] Y. Tian, D. Krishnan, and P. Isola. Contrastive multiview coding. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XI 16, pages 776-794. Springer, 2020.
[45] USGS. Lansat missions. https://www.usgs.gov/landsat-missions, 2013.
[46] Y. Wang, C. M. Albrecht, N. Braham, L. Mou, and X. X. Zhu. Self-supervised learning in remote sensing. IEEE Geoscience and Remote Sensing Magazine, December:213-247, 2022.
[47] G.-S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, and X. Lu. Aid: A benchmark data set for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7):3965-3981, 2017.
[48] S. Yan, X. Xu, D. Xu, S. Lin, and X. Li. Beyond spatial pyramids: A new feature extraction framework with dense spatial sampling for image classification. In Computer Vision-ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV 12, pages 473-487. Springer, 2012.
[49] S. Yan, X. Xu, D. Xu, S. Lin, and X. Li. Image classification with densely sampled image windows and generalized adaptive multiple kernel learning. IEEE Transactions on Cybernetics, 45(3):381-390, 2014.
[50] J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pages 12310-12320. PMLR, 2021.
[51] C. Zhang, K. Zhang, C. Zhang, T. X. Pham, C. D. Yoo, and I. S. Kweon. How does Simsiam avoid collapse without negative samples? a unified understanding with self-supervised contrastive learning. arXiv preprint arXiv:2203.16262, 2022.
[52] D. Zhou, R. Duan, L. Zhao, and X. Chai. Single image super-resolution reconstruction based on multi-scale feature mapping adversarial network. Signal Processing, 166:107251, 2020.
[53] Y. Zou and L. Cheng. A transfer learning model for gesture recognition based on the deep features extracted by cnn. IEEE Transactions on Artificial Intelligence, 2(5):447-458, 2021.