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However, clouds pose a significant challenge, occluding the objects on satellite RS images. In addition, snow coverage mapping plays a vital role in studying hydrology and climatology and investigating crop disease overwintering for smart agriculture. Distinguishing snow from clouds is challenging since they share similar color and reflection characteristics. Conventional approaches with manual thresholding and machine learning algorithms (e.g., SVM and Random Forest) could not fully extract useful information, while current deep-learning methods, e.g., CNNs or Transformer models, still have limitations in fully exploiting abundant spatial\/spectral information of RS images. Therefore, this work aims to develop an efficient snow and cloud classification algorithm using satellite multispectral RS images. In particular, we propose an innovative algorithm entitled UCTNet by adopting a dual-flow structure to integrate information extracted via Transformer and CNN branches. Particularly, CNN and Transformer integration Module (CTIM) is designed to maximally integrate the information extracted via two branches. Meanwhile, Final Information Fusion Module and Auxiliary Information Fusion Head are designed for better performance. The four-band satellite multispectral RS dataset for snow coverage mapping is adopted for performance evaluation. Compared with previous methods (e.g., U-Net, Swin, and CSDNet), the experimental results show that the proposed UCTNet achieves the best performance in terms of accuracy (95.72%) and mean IoU score (91.21%) while with the smallest model size (3.93 M). The confirmed efficiency of UCTNet shows great potential for dual-flow architecture on snow and cloud classification.<\/jats:p>","DOI":"10.3390\/rs15174213","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T05:46:47Z","timestamp":1693201607000},"page":"4213","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["UCTNet with Dual-Flow Architecture: Snow Coverage Mapping with Sentinel-2 Satellite Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Jinge","family":"Ma","sequence":"first","affiliation":[{"name":"School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanxiu","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0996-2586","authenticated-orcid":false,"given":"Tianxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"},{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3121-7208","authenticated-orcid":false,"given":"Jinya","family":"Su","sequence":"additional","affiliation":[{"name":"School of Automation, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Hua","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangyun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"},{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Munawar, H.S., Ullah, F., Qayyum, S., Khan, S.I., and Mojtahedi, M. (2021). Uavs in disaster management: Application of integrated aerial imagery and convolutional neural network for flood detection. Sustainability, 13.","DOI":"10.3390\/su13147547"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1016\/j.jclepro.2017.10.294","article-title":"Modeling and evaluating land-use\/land-cover change for urban planning and sustainability: A case study of Dongying city, China","volume":"172","author":"Wang","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cai, G., Ren, H., Yang, L., Zhang, N., Du, M., and Wu, C. (2019). Detailed urban land use land cover classification at the metropolitan scale using a three-layer classification scheme. Sensors, 19.","DOI":"10.3390\/s19143120"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/LGRS.2015.2439696","article-title":"Accurate urban area detection in remote sensing images","volume":"12","author":"Shi","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, T., Su, J., Xu, Z., Luo, Y., and Li, J. (2021). Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Appl. Sci., 11.","DOI":"10.3390\/app11020543"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"035002","DOI":"10.1088\/2515-7620\/abd836","article-title":"A global assessment of wildfire potential under climate change utilizing Keetch-Byram drought index and land cover classifications","volume":"3","author":"Gannon","year":"2021","journal-title":"Environ. Res. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"190255","DOI":"10.4491\/eer.2019.255","article-title":"Glacier changes monitoring in Bhutan High Himalaya using remote sensing technology","volume":"26","author":"Kumar","year":"2020","journal-title":"Environ. Eng. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s11633-018-1143-x","article-title":"Potential bands of sentinel-2A satellite for classification problems in precision agriculture","volume":"16","author":"Zhang","year":"2019","journal-title":"Int. J. Autom. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19105","DOI":"10.1029\/2003JD004457","article-title":"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","volume":"109","author":"Zhang","year":"2004","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14415","DOI":"10.1038\/s41598-022-18812-6","article-title":"Cloud and Snow Detection of Remote Sensing Images Based on Improved Unet3","volume":"12","author":"Yin","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Su, J., Zhai, X., Meng, F., and Liu, C. (2022). Snow coverage mapping by learning from sentinel-2 satellite multispectral images via machine learning algorithms. Remote Sens., 14.","DOI":"10.3390\/rs14030782"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1109\/TII.2020.2979237","article-title":"Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring","volume":"17","author":"Su","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.14358\/PERS.72.10.1179","article-title":"Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm","volume":"72","author":"Irish","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6169","DOI":"10.1029\/2019WR024932","article-title":"Cloud masking for Landsat 8 and MODIS Terra over snow-covered terrain: Error analysis and spectral similarity between snow and cloud","volume":"55","author":"Stillinger","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bai, T., Li, D., Sun, K., Chen, Y., and Li, W. (2016). Cloud detection for high-resolution satellite imagery using machine learning and multi-feature fusion. Remote Sens., 8.","DOI":"10.3390\/rs8090715"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nijhawan, R., Raman, B., and Das, J. (2017, January 9\u201312). Meta-classifier approach with ANN, SVM, rotation forest, and random forest for snow cover mapping. Proceedings of the 2nd International Conference on Computer Vision & Image Processing, Roorkee, India.","DOI":"10.1007\/978-981-10-7898-9_23"},{"key":"ref_20","unstructured":"Ghasemian, N., and Akhoondzadeh, M. (2018, January 16\u201318). Integration of VIR and thermal bands for cloud, snow\/ice and thin cirrus detection in MODIS satellite images. Proceedings of the Third International Conference on Intelligent Decision Science, Tehran, Iran."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, Y., Tang, L., Fan, R., and Yao, Y. (2018). Object-based convolutional neural networks for cloud and snow detection in high-resolution multispectral imagers. Water, 10.","DOI":"10.3390\/w10111666"},{"key":"ref_23","first-page":"33","article-title":"Cloud segmentation in Advanced Wide Field Sensor (AWiFS) data products using deep learning approach","volume":"16","author":"Mohapatra","year":"2022","journal-title":"J. Geomat."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1109\/LGRS.2017.2735801","article-title":"Distinguishing cloud and snow in satellite images via deep convolutional network","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TGRS.2019.2934760","article-title":"HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers","volume":"58","author":"He","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"102837","article-title":"CapViT: Cross-context capsule vision transformers for land cover classification with airborne multispectral LiDAR data","volume":"111","author":"Yu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhang, W., Zhang, T., Yang, Z., and Li, J. (2021). Efficient transformer for remote sensing image segmentation. Remote Sens., 13.","DOI":"10.3390\/rs13183585"},{"key":"ref_29","first-page":"1","article-title":"A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_32","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2021). Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","unstructured":"Loshchilov, I., and Hutter, F. (May, January 30). Decoupled Weight Decay Regularization. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, G., Gao, X., Yang, Y., Wang, M., and Ran, S. (2021). Controllably Deep Supervision and Multi-Scale Feature Fusion Network for Cloud and Snow Detection Based on Medium-and High-Resolution Imagery Dataset. Remote Sens., 13.","DOI":"10.3390\/rs13234805"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_37","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_38","first-page":"15475","article-title":"ResT: An efficient transformer for visual recognition","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4213\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:40:21Z","timestamp":1760128821000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,27]]},"references-count":38,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174213"],"URL":"https:\/\/doi.org\/10.3390\/rs15174213","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,27]]}}}