Cross-supervised learning for cloud detection

References

• Chen, L.-C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2017. “Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (4): 834–21.
   PubMed Web of Science ®Google Scholar
• Foga, S., P. L. Scaramuzza, S. Guo, Z. Zhu, R. D. Dilley Jr, T. Beckmann, G. L. Schmidt, J. L. Dwyer, M. J. Hughes, and B. Laue. 2017. “Cloud Detection Algorithm Comparison and Validation for Operational Landsat Data Products.” Remote Sensing of Environment 194: 379–390. doi:10.1016/j.rse.2017.03.026.
   Web of Science ®Google Scholar
• Guo, J., Q. Xu, Y. Zeng, Z. Liu, and X. Zhu. 2022. “Semi-Supervised Cloud Detection in Satellite Images by considering the Domain Shift Problem.” Remote Sensing 14 (11): 2641. doi:10.3390/rs14112641.
   Web of Science ®Google Scholar
• Guo, J., J. Yang, H. Yue, X. Liu, and K. Li. 2021. “Unsupervised Domain-Invariant Feature Learning for Cloud Detection of Remote Sensing Images.” IEEE Transactions on Geoscience and R Emote Sensing 60: 1–15.
   Google Scholar
• Guo, J., J. Yang, H. Yue, H. Tan, C. Hou, and K. Li. 2020. “CDnetV2: CNN-based Cloud Detection for Remote Sensing Imagery with cloud-snow Coexistence.” IEEE Transactions on Geoscience and Remote Sensing 59 (1): 700–713. doi:10.1109/TGRS.2020.2991398.
   Web of Science ®Google Scholar
• Hadjimitsis, D. G., C. R. Clayton, and A. Retails. 2009. “The Use of Selected Pseudo- invariant Targets for the Application of Atmospheric Correction in multi-temporal Studies Using Satellite Remotely Sensed Imagery.” International Journal of Applied Earth Observation and Geoinformation 11 (3): 192–200. doi:10.1016/j.jag.2009.01.005.
   Web of Science ®Google Scholar
• Han, L., P. Li, A. Plaza, and P. Ren. 2021. “Hashing for Localization (Hfl): A Baseline for Fast Localizing Objects in A Large-Scale Scene.” IEEE Transactions on Geoscience and R Emote Sensing 60: 1–16.
   Web of Science ®Google Scholar
• He, Q., X. Sun, Z. Yan, and K. Fu. 2021. “DABNet: Deformable Contextual and boundary-weighted Network for Cloud Detection in Remote Sensing Images.” IEEE Transactions on Geoscience and R Emote Sensing 60: 1–16.
   Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2015. “Delving Deep into Rectifiers: Surpassing human-level Performance on Imagenet Classification.” 2015 IEEE international conference on computer vision (ICCV), Santiago, Chile, 1026–1034. doi:10.1109/ICCV.2015.123.
   Google Scholar
• Hu, X., Y. Wang, and J. Shan. 2015. “Automatic Recognition of Cloud Images by Using Visual Saliency Features.” IEEE Geoscience and Remote Sensing Letters 12 (8): 1760–1764. doi:10.1109/LGRS.2015.2424531.
   Web of Science ®Google Scholar
• Ioffe, S., and C. Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.” International Conference on Machine Learning 448–456. PMLR
   Google Scholar
• Jeppesen, J. H., R. H. Jacobsen, F. Inceoglu, and T. S. Toftegaard. 2019. “A Cloud Detection Algorithm for Satellite Imagery Based on Deep Learning.” Remote Sensing of Environment 229: 247–259. doi:10.1016/j.rse.2019.03.039.
   Web of Science ®Google Scholar
• Kingma, D. P., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.” arXiv preprint arXiv:1412.6980.
   Google Scholar
• Kriebel, K.-T., G. Gesell, M. Ka Stner, and H. Mannstein. 2003. “The Cloud Analysis Tool APOLLO: Improvements and Validations.” International Journal of Remote Sensing 24 (12): 2389–2408. doi:10.1080/01431160210163065.
   Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, G. E. Hinton, and E. P. Simoncelli. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems 25: 3113–3121.
   PubMedGoogle Scholar
• Laine, S., and T. Aila. 2016. “Temporal Ensembling for semi-supervised Learning.” arXiv preprint arXiv:1610.02242.
   Google Scholar
• LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” nature 521 (7553): 436–444. doi:10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. “Gradient-based Learning Applied to Document Recognition.” Proceedings of the IEEE 86 (11): 2278–2324. 10.1109/5.726791
   Web of Science ®Google Scholar
• Lin, C.-H., B.-Y. Lin, K.-Y. Lee, and Y.-C. Chen. 2015. “Radiometric Normalization and Cloud Detection of Optical Satellite Images Using invariant Pixels.” ISPRS Journal of Photogrammetry and Remote Sensing 106: 107–117. doi:10.1016/j.isprsjprs.2015.05.003.
   Web of Science ®Google Scholar
• Li, Z., H. Shen, Q. Cheng, Y. Liu, S. You, and Z. He. 2019. “Deep Learning Based Cloud Detection for Medium and High Resolution Remote Sensing Images of Different Sensors.” ISPRS Journal of Photogrammetry and Remote Sensing 150: 197–212. doi:10.1016/j.isprsjprs.2019.02.017.
   Web of Science ®Google Scholar
• Li, Z., H. Shen, H. Li, G. Xia, P. Gamba, and L. Zhang. 2017b. “Multi-feature Combined Cloud and Cloud Shadow Detection in GaoFen-1 Wide Field of View Imagery.” Remote Sensing of Environment 191: 342–358. doi:10.1016/j.rse.2017.01.026.
   Web of Science ®Google Scholar
• Li, Z., H. Shen, Q. Weng, Y. Zhang, P. Dou, and L. Zhang. 2022. “Cloud and Cloud Shadow Detection for Optical Satellite Imagery: Features, Algorithms, Validation, and Prospects.” ISPRS Journal of Photogrammetry and Remote Sensing 188: 89–108. doi:10.1016/j.isprsjprs.2022.03.020.
   Web of Science ®Google Scholar
• Li, Y., Y. Zhang, X. Huang, H. Zhu, and J. Ma. 2017a. “Large-scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing 56 (2): 950–965. doi:10.1109/TGRS.2017.2756911.
   Web of Science ®Google Scholar
• Löw, F., D. Dimov, S. Kenjabaev, S. Zaitov, G. Stulina, and V. Dukhovny. 2022. “Land Cover Change Detection in the Aralkum with multi-source Satellite Datasets.” GIScience & Remote Sensing 59 (1): 17–35. doi:10.1080/15481603.2021.2009232.
   Web of Science ®Google Scholar
• Luo, C., S. Feng, X. Yang, Y. Ye, X. Li, B. Zhang, Z. Chen, and Y. Quan. 2022. “LWCDnet: A Lightweight Network for Efficient Cloud Detection in Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–16. doi:10.1109/TGRS.2022.3173661.
   Web of Science ®Google Scholar
• Luotamo, M., S. Metsämäki, and A. Klami. 2020. “Multiscale Cloud Detection in Remote Sensing Images Using a Dual Convolutional Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 59 (6): 4972–4983. doi:10.1109/TGRS.2020.3015272.
   Web of Science ®Google Scholar
• Mohajerani, S., and P. Saeedi. 2019. “Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery.“ 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 1029–1032. doi:10.1109/IGARSS.2019.8898776.
   Google Scholar
• Niederheiser, R., M. Winkler, V. Di Cecco, B. Erschbamer, R. Fernández, C. Geitner, H. Hofbauer, et al. 2021. “Using Automated Vegetation Cover Estimation from Close- Range Photogrammetric Point Clouds to Compare Vegetation Location Properties in Mountain Terrain.” GIScience & Remote Sensing 58 (1): 120–137. DOI:10.1080/15481603.2020.1859264.
   Web of Science ®Google Scholar
• Noble, W. S. 2006. “What Is a Support Vector Machine?” Nature Biotechnology 24 (12): 1565–1567. doi:10.1038/nbt1206-1565.
   PubMed Web of Science ®Google Scholar
• Pan, B., Z. Shi, and X. Xu. 2017. “Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 55 (7): 4177–4189. doi:10.1109/TGRS.2017.2689805.
   Web of Science ®Google Scholar
• Paszke, A., S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, et al. 2019. “Pytorch: An Imperative Style, high-performance Deep Learning Library.” Advances in Neural Information Processing Systems. 32.
   Google Scholar
• Rossow, W. B., and L. C. Garder. 1993. “Cloud Detection Using Satellite Measurements of Infrared and Visible Radiances for ISCCP.” Journal of Climate 6 (12): 2341–2369. doi:10.1175/1520-0442(1993)006<2341:CDUSMO>2.0.CO;2.
   Web of Science ®Google Scholar
• Shi, C., C. Wang, Y. Wang, and B. Xiao. 2017. “Deep Convolutional activations-based Features for ground-based Cloud Classification.” IEEE Geoscience and Remote Sensing Letters 14 (6): 816–820. doi:10.1109/LGRS.2017.2681658.
   Web of Science ®Google Scholar
• Smith, A. R. 1979. “Tint Fill.” The 6th Annual Conference on Computer Graphics and Interactive Techniques, Chicago, USA, 276–283. doi:10.1145/965103.807456.
   Google Scholar
• Tai, X., M. Li, M. Xiang, and P. Ren. 2022. “A Mutual Guide Framework for Training Hyperspectral Image Classifiers with Small Data.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–17. doi:10.1109/TGRS.2021.3092351.
   Web of Science ®Google Scholar
• Tan, M., and Q. Le. 2019. “Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks.” 2019 International Conference on Machine Learning, Long Beach, CA, USA, 6105–6114.
   Google Scholar
• Tarjan, R. 1972. “Depth-first Search and Linear Graph Algorithms.” SIAM Journal on Computing 1 (2): 146–160. doi:10.1137/0201010.
   Google Scholar
• Tarvainen, A., and H. Valpola. 2017. “Mean Teachers are Better Role Models: Weight- Averaged Consistency Targets Improve semi-supervised Deep Learning Results.” Advances in Neural Information Processing Systems 30.
   Google Scholar
• Wold, S., K. Esbensen, and P. Geladi. 1987. “Principal Component Analysis.” Chemometrics and Intelligent Laboratory Systems 2 (1–3): 37–52. doi:10.1016/0169-7439(87)80084-9.
   Web of Science ®Google Scholar
• Wu, K., Z. Xu, X. Lyu, and P. Ren. 2022. “Cloud Detection with Boundary Nets.” ISPRS Journal of Photogrammetry and Remote Sensing 186: 218–231. doi:10.1016/j.isprsjprs.2022.02.010.
   Web of Science ®Google Scholar
• Xie, F., M. Shi, Z. Shi, J. Yin, and D. Zhao. 2017. “Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (8): 3631–3640. doi:10.1109/JSTARS.2017.2686488.
   Web of Science ®Google Scholar
• Yang, J., J. Guo, H. Yue, Z. Liu, H. Hu, and K. Li. 2019. “CDnet: CNN-based Cloud Detection for Remote Sensing Imagery.” IEEE Transactions on Geoscience and Remote Sensing 57 (8): 6195–6211. doi:10.1109/TGRS.2019.2904868.
   Web of Science ®Google Scholar
• Yan, Z., M. Yan, H. Sun, K. Fu, J. Hong, J. Sun, Y. Zhang, and X. Sun. 2018. “Cloud and Cloud Shadow Detection Using Multilevel Feature Fused Segmentation Network.” IEEE Geoscience and Remote Sensing Letters 15 (10): 1600–1604. doi:10.1109/LGRS.2018.2846802.
   Web of Science ®Google Scholar
• Yu, X., X. Wu, C. Luo, and P. Ren. 2017. “Deep Learning in Remote Sensing Scene Classification: A Data Augmentation Enhanced Convolutional Neural Network Framework.” GIScience & Remote Sensing 54 (5): 741–758. doi:10.1080/15481603.2017.1323377.
   Web of Science ®Google Scholar
• Zhang, Y., W. B. Rossow, A. A. Lacis, V. Oinas, and M. I. Mishchenko. 2004. “Calculation of Radiative Fluxes from the Surface to Top of Atmosphere Based on ISCCP and Other Global Data Sets: Refinements of the Radiative Transfer Model and the Input Data.” Journal of Geophysical Research: A Tmospheres 109 (D19). doi:10.1029/2003JD004457.
   Google Scholar
• Zhao, H., J. Shi, X. Qi, X. Wang, and J. Jia. 2017. “Pyramid Scene Parsing Network.” 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, 2881–2890. doi:10.1109/CVPR.2017.660.
   Google Scholar
• Zhu, Z., S. Wang, and C. E. Woodcock. 2015. “Improvement and Expansion of the Fmask Algorithm: Cloud, Cloud Shadow, and Snow Detection for Landsats 4–7, 8, and Sentinel 2 Images.” Remote Sensing of Environment 159: 269–277. doi:10.1016/j.rse.2014.12.014.
   Web of Science ®Google Scholar
• Zhu, Z., and C. E. Woodcock. 2012. “Object-based Cloud and Cloud Shadow Detection in Landsat Imagery.” Remote Sensing of Environment 118: 83–94. doi:10.1016/j.rse.2011.10.028.
   Web of Science ®Google Scholar