{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:54:45Z","timestamp":1778860485058,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T00:00:00Z","timestamp":1694995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41775165"],"award-info":[{"award-number":["41775165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41775039"],"award-info":[{"award-number":["41775039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021r034"],"award-info":[{"award-number":["2021r034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023AH020022"],"award-info":[{"award-number":["2023AH020022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KYCX23_1364"],"award-info":[{"award-number":["KYCX23_1364"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013156","name":"Startup Foundation for Introducing Talent of NUIST","doi-asserted-by":"publisher","award":["41775165"],"award-info":[{"award-number":["41775165"]}],"id":[{"id":"10.13039\/501100013156","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013156","name":"Startup Foundation for Introducing Talent of NUIST","doi-asserted-by":"publisher","award":["41775039"],"award-info":[{"award-number":["41775039"]}],"id":[{"id":"10.13039\/501100013156","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013156","name":"Startup Foundation for Introducing Talent of NUIST","doi-asserted-by":"publisher","award":["2021r034"],"award-info":[{"award-number":["2021r034"]}],"id":[{"id":"10.13039\/501100013156","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013156","name":"Startup Foundation for Introducing Talent of NUIST","doi-asserted-by":"publisher","award":["2023AH020022"],"award-info":[{"award-number":["2023AH020022"]}],"id":[{"id":"10.13039\/501100013156","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013156","name":"Startup Foundation for Introducing Talent of NUIST","doi-asserted-by":"publisher","award":["KYCX23_1364"],"award-info":[{"award-number":["KYCX23_1364"]}],"id":[{"id":"10.13039\/501100013156","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui Provincial University Outstanding Youth Research Project","award":["41775165"],"award-info":[{"award-number":["41775165"]}]},{"name":"Anhui Provincial University Outstanding Youth Research Project","award":["41775039"],"award-info":[{"award-number":["41775039"]}]},{"name":"Anhui Provincial University Outstanding Youth Research Project","award":["2021r034"],"award-info":[{"award-number":["2021r034"]}]},{"name":"Anhui Provincial University Outstanding Youth Research Project","award":["2023AH020022"],"award-info":[{"award-number":["2023AH020022"]}]},{"name":"Anhui Provincial University Outstanding Youth Research Project","award":["KYCX23_1364"],"award-info":[{"award-number":["KYCX23_1364"]}]},{"name":"Jiangsu Province Graduate Research Innovation Program Project","award":["41775165"],"award-info":[{"award-number":["41775165"]}]},{"name":"Jiangsu Province Graduate Research Innovation Program Project","award":["41775039"],"award-info":[{"award-number":["41775039"]}]},{"name":"Jiangsu Province Graduate Research Innovation Program Project","award":["2021r034"],"award-info":[{"award-number":["2021r034"]}]},{"name":"Jiangsu Province Graduate Research Innovation Program Project","award":["2023AH020022"],"award-info":[{"award-number":["2023AH020022"]}]},{"name":"Jiangsu Province Graduate Research Innovation Program Project","award":["KYCX23_1364"],"award-info":[{"award-number":["KYCX23_1364"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cloud observation serves as the fundamental bedrock for acquiring comprehensive cloud-related information. The categorization of distinct ground-based clouds holds profound implications within the meteorological domain, boasting significant applications. Deep learning has substantially improved ground-based cloud classification, with automated feature extraction being simpler and far more accurate than using traditional methods. A reengineering of the DenseNet architecture has given rise to an innovative cloud classification method denoted as CloudDenseNet. A novel CloudDense Block has been meticulously crafted to amplify channel attention and elevate the salient features pertinent to cloud classification endeavors. The lightweight CloudDenseNet structure is designed meticulously according to the distinctive characteristics of ground-based clouds and the intricacies of large-scale diverse datasets, which amplifies the generalization ability and elevates the recognition accuracy of the network. The optimal parameter is obtained by combining transfer learning with designed numerous experiments, which significantly enhances the network training efficiency and expedites the process. The methodology achieves an impressive 93.43% accuracy on the large-scale diverse dataset, surpassing numerous published methods. This attests to the substantial potential of the CloudDenseNet architecture for integration into ground-based cloud classification tasks.<\/jats:p>","DOI":"10.3390\/s23187957","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T03:02:17Z","timestamp":1695092537000},"page":"7957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["CloudDenseNet: Lightweight Ground-Based Cloud Classification Method for Large-Scale Datasets Based on Reconstructed DenseNet"],"prefix":"10.3390","volume":"23","author":[{"given":"Sheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Zhuang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"737","DOI":"10.5194\/amt-14-737-2021","article-title":"Improving Cloud Type Classification of Ground-Based Images Using Region Covariance Descriptors","volume":"14","author":"Tang","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8129","DOI":"10.1175\/JCLI-D-15-0700.1","article-title":"The Relationship between Boundary Layer Stability and Cloud Cover in the Post-Cold Frontal Region","volume":"29","author":"Naud","year":"2016","journal-title":"J. Clim."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"388","DOI":"10.4028\/www.scientific.net\/AMR.1073-1076.388","article-title":"Prediction of Regional Global Horizontal Irradiance Combining Ground-Based Cloud Observation and Numerical Weather Prediction","volume":"1073","author":"Cui","year":"2014","journal-title":"Adv. Mater. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yuan, F., Lee, Y.H., and Meng, Y.S. (2014, January 6\u201311). Comparison of Radio-Sounding Profiles for Cloud Attenuation Analysis in the Tropical Region. Proceedings of the 2014 IEEE Antennas and Propagation Society International Symposium (APSURSI), Memphis, TN, USA.","DOI":"10.1109\/APS.2014.6904461"},{"key":"ref_5","first-page":"173","article-title":"A Deep Learning Approach for Classification of Cloud Image Patches on Small Datasets","volume":"16","author":"Phung","year":"2018","journal-title":"J. Inf. Commun. Converg. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yu, A., Tang, M., Li, G., Hou, B., Xuan, Z., Zhu, B., and Chen, T. (2021). A Novel Robust Classification Method for Ground-Based Clouds. Atmosphere, 12.","DOI":"10.3390\/atmos12080999"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4836","DOI":"10.1080\/01431161.2023.2240034","article-title":"CloudDeepLabV3+: A Lightweight Ground-Based Cloud Segmentation Method Based on Multi-Scale Feature Aggregation and Multi-Level Attention Feature Enhancement","volume":"44","author":"Li","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.5194\/amt-8-1173-2015","article-title":"Block-Based Cloud Classification with Statistical Features and Distribution of Local Texture Features","volume":"8","author":"Cheng","year":"2015","journal-title":"Atmos. Meas. Tech."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, X., Chi, M., Zhang, Y., and Qin, Y. (2018, January 22\u201327). Classifying High Resolution Remote Sensing Images by Fine-Tuned VGG Deep Networks. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518078"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1175\/JTECH-D-13-00209.1","article-title":"A Smart Image-Based Cloud Detection System for Intrahour Solar Irradiance Forecasts","volume":"31","author":"Chu","year":"2014","journal-title":"J. Atmos. Oceanic Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.solener.2015.05.037","article-title":"3D Cloud Detection and Tracking System for Solar Forecast Using Multiple Sky Imagers","volume":"118","author":"Peng","year":"2015","journal-title":"Solar Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"557","DOI":"10.5194\/amt-3-557-2010","article-title":"Automatic Cloud Classification of Whole Sky Images","volume":"3","author":"Heinle","year":"2010","journal-title":"Atmos. Meas. Tech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1175\/2007JTECHA959.1","article-title":"Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition","volume":"25","author":"Sabburg","year":"2008","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"095062","DOI":"10.1117\/1.JRS.9.095062","article-title":"Ground-Based Cloud Classification Using Weighted Local Binary Patterns","volume":"9","author":"Liu","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5729","DOI":"10.1109\/TGRS.2017.2712809","article-title":"Deepcloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features","volume":"55","author":"Ye","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8665","DOI":"10.1029\/2018GL077787","article-title":"CloudNet: Ground-Based Cloud Classification with Deep Convolutional Neural Network","volume":"45","author":"Zhang","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2020GL087338","DOI":"10.1029\/2020GL087338","article-title":"Ground-Based Cloud Classification Using Task-Based Graph Convolutional Network","volume":"47","author":"Liu","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6034","DOI":"10.1109\/JIOT.2018.2866328","article-title":"Multimodal GAN for Energy Efficiency and Cloud Classification in Internet of Things","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.1109\/JSTARS.2017.2669206","article-title":"Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding","volume":"10","author":"Gan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"63081","DOI":"10.1109\/ACCESS.2019.2916905","article-title":"Dual Guided Loss for Ground-Based Cloud Classification in Weather Station Networks","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Phung, V.H., and Rhee, E.J. (2019). A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets. Appl. Sci., 9.","DOI":"10.3390\/app9214500"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, X., Qiu, B., Cao, G., Wu, C., and Zhang, L. (2022). A Novel Method for Ground-Based Cloud Image Classification Using Transformer. Remote Sens., 14.","DOI":"10.3390\/rs14163978"},{"key":"ref_23","first-page":"1","article-title":"Ground-Based Remote Sensing Cloud Classification via Context Graph Attention Network","volume":"99","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"World Meteorological Organization (2023, August 19). International Cloud Atlas Manual on the Observation of Clouds and Other Meteors. Available online: https:\/\/cloudatlas.wmo.int\/home.html\/."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fang, C., Jia, K., Liu, P., and Zhang, L. (2019, January 18\u201321). Research on cloud recognition technology based on transferlearning. Proceedings of the 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Lanzhou, China.","DOI":"10.1109\/APSIPAASC47483.2019.9023267"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A Comprehensive Survey on Transfer Learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Naushad, R., Kaur, T., and Ghaderpour, E. (2021). Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study. Sensors, 21.","DOI":"10.3390\/s21238083"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, Y., and Newsam, S. (2017, January 17\u201320). Densenet for Dense Flow. Proceedings of the IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296389"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Muhammad, U., Wang, W., Chattha, S.P., and Ali, S. (2018, January 20\u201324). Pre-Trained VGGNet Architecture for Remote-Sensing Image Scene Classification. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545591"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","article-title":"Wider or Deeper: Revisiting the ResNet Model for Visual Recognition","volume":"90","author":"Wu","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sinha, D., and El-Sharkawy, M. (2019, January 24\u201326). Thin MobileNet: An Enhanced MobileNet Architecture. Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON47517.2019.8993089"},{"key":"ref_32","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"146533","DOI":"10.1109\/ACCESS.2019.2946000","article-title":"Pulmonary Image Classification Based on Inception-V3 Transfer Learning Model","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dev, S., Lee, Y.H., and Winkler, S. (2015, January 27\u201330). Categorization of Cloud Image Patches Using an Improved Texton-Based Approach. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350833"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5513","DOI":"10.1007\/s00521-021-06714-z","article-title":"An Automatic Plant Leaf Disease Identification Using DenseNet-121 Architecture with a Mutation-Based Henry Gas Solubility Optimization Algorithm","volume":"34","author":"Nandhini","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Luo, C., Zhan, J., Xue, X., Wang, L., Ren, R., and Yang, Q. (2018, January 4\u20137). Cosine normalization: Using cosine similarity instead of dot product in neural networks. Proceedings of the Artificial Neural Networks and Machine Learning\u2014ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece.","DOI":"10.1007\/978-3-030-01418-6_38"},{"key":"ref_38","unstructured":"LeCun, Y., Bottou, L., Orr, G.B., and M\u00fcller, K.R. (2012). Neural Networks: Tricks of the Trade, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2196096","DOI":"10.1155\/2022\/2196096","article-title":"Deep Learning-Based Classification for Melanoma Detection Using XceptionNet","volume":"2022","author":"Lu","year":"2022","journal-title":"J. Healthc. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7957\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:53:04Z","timestamp":1760129584000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,18]]},"references-count":39,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187957"],"URL":"https:\/\/doi.org\/10.3390\/s23187957","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,18]]}}}