Abstract
Accurate cloud detection and segmentation in satellite imagery are critical for applications such as weather forecasting, environmental monitoring, and disaster management. Traditional methods often struggle with the variability and complexity of cloud formations, leading to limitations in accuracy and efficiency. This project addresses these challenges by leveraging deep learning techniques, specifically the ResNet-50 architecture integrated with U-Net, to enhance the precision and robustness of cloud detection. The model is trained on the 38-Cloud dataset, which includes multi-spectral satellite images with pixel-level annotations, enabling effective differentiation between cloud types and other atmospheric features. The proposed system emphasizes deployment on edge devices, such as NVIDIA Jetson Nano, to facilitate real-time processing and analysis directly within satellites, reducing latency and enabling continuous monitoring without the need for constant ground-based data transmission. The model’s performance is rigorously evaluated using metrics such as Intersection over Union (IoU), Dice Coefficient, precision, recall, and F1-score, demonstrating high accuracy and reliability. This work contributes to the advancement of real-time atmospheric analysis, offering a scalable and efficient solution for global weather prediction and disaster response. The integration of a user-friendly web interface further enhances accessibility, making this tool valuable for researchers and practitioners in remote sensing and related fields.
References
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.
Zhang, Z., Liu, Q., and Wang, Y. (2018). Road Extraction by Deep Residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15(5), 749–753.
Yuan, K., Meng, G., Cheng, D., Bai, J., Xiang, S., and Pan, C. (2017). Efficient Cloud Detection in Remote Sensing Images Using Edge-aware Segmentation Network and Easy-to-hard Training Strategy. 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, pp. 61–65.
Yang, J., Guo, J., Yue, H., Liu, Z., Hu, H., and Li, K. (2019). CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(8), 6195–6211.
Google Earth Engine Developers. (n.d.). Sentinel-2 Cloud Masking with s2cloudless. Retrieved from https://developers.google.com/earth-engine/tutorials/community/sentinel-2.
Hu, K., Zhang, D., and Xia, M. (2021). CDUNet: Cloud Detection UNet for Remote Sensing Imagery. Remote Sens., 13(4533). https://doi.org/10.3390/rs13224533.
Yan, Z., et al. (2018). Cloud and Cloud Shadow Detection Using Multilevel Feature Fused Segmentation Network. IEEE Geoscience and Remote Sensing Letters, 15(10), 1600–1604. https://doi.org/10.1109/LGRS.2018.2846802.
Gonzales, C., and Sakla, W. (2019). Semantic Segmentation of Clouds in Satellite Imagery Using Deep Pre-trained U-Nets. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA, pp. 1–7. https://doi.org/10.1109/AIPR47015.2019.9174594.
Sorour. (2021). 38Cloud: Cloud Segmentation in Satellite Images. Retrieved from https://www.kaggle.com/datasets/sorour/38cloud-cloud-segmentation-in-satellite-images.
Mahajan, S., and Fataniya, B. (2020). Cloud Detection Methodologies: Variants and Development – A Review. Complex Intell. Syst., 6, 251–261. https://doi.org/10.1007/s40747-019-00128-0.
Li, X., and Zhang, H. (2022). Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1234–1245.
Zhao, Y., and Li, M. (2022). Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 182.
Sawant, M., Shende, M.K., Feijóo-Lorenzo, A.E., and Bokde, N.D. (2021). The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review. Energies, 14, 8119. https://doi.org/10.3390/en14238119.
Zhang, Q., Cui, Z., Niu, X., Geng, S., and Qiao, Y. (2017). Image Segmentation with Pyramid Dilated Convolution Based on ResNet and U-Net. In: Liu, D., Xie, S., Li, Y., Zhao, D., and El-Alfy, E.S. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science, vol. 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-038.
Wang, Z.; Zhao, L.; Meng, J.; Han, Y.; Li, X.; Jiang, R.; Chen, J.; Li, H. Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey. Remote Sens. 2024, 16, 4583. https://doi.org/10.3390/rs16234583.
Ni, Z.; Wu, M.; Lu, Q.; Huo, H.; Wu, C.; Liu, R.; Wang, F.; Xu, X. A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances. Remote Sens. 2024, 16, 4629. https://doi.org/10.3390/rs16244629.