{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T05:54:02Z","timestamp":1775886842716,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Fahd University of Petroleum &amp; Minerals (KFUPM)","award":["JRC-AI-RFP-17"],"award-info":[{"award-number":["JRC-AI-RFP-17"]}]},{"name":"SDAIA-KFUPM Joint Research Center for Artificial Intelligence","award":["JRC-AI-RFP-17"],"award-info":[{"award-number":["JRC-AI-RFP-17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A significant amount of satellite imaging data is now easily available due to the continued development of remote sensing (RS) technology. Enabling the successful application of RS in real-world settings requires efficient and scalable solutions to extend their use in multidisciplinary areas. The goal of quick analysis and precise classification in Remote Sensing Imaging (RSI) is often accomplished by utilizing approaches based on deep Convolution Neural Networks (CNNs). This research offers a unique snapshot-based residual network (SnapResNet) that consists of fully connected layers (FC-1024), batch normalization (BN), L2 regularization, dropout layers, dense layer, and data augmentation. Architectural changes overcome the inter-class similarity problem while data augmentation resolves the problem of imbalanced classes. Moreover, the snapshot ensemble technique is utilized to prevent over-fitting, thereby further improving the network\u2019s performance. The proposed SnapResNet152 model employs the most challenging Large-Scale Cloud Images Dataset for Meteorology Research (LSCIDMR), having 10 classes with thousands of high-resolution images and classifying them into respective classes. The developed model outperforms the existing deep learning-based algorithms (e.g., AlexNet, VGG-19, ResNet101, and EfficientNet) and achieves an overall accuracy of 97.25%.<\/jats:p>","DOI":"10.3390\/rs15235597","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T08:36:59Z","timestamp":1701419819000},"page":"5597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Satellite Imagery-Based Cloud Classification Using Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Rukhsar","family":"Yousaf","sequence":"first","affiliation":[{"name":"Institute of Avionics & Aeronautics (IAA), Air University (AU), Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1623-5317","authenticated-orcid":false,"given":"Hafiz Zia Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Department of Mechatronics and Biomedical Engineering, Air University (AU), Islamabad 44000, Pakistan"}]},{"given":"Khurram","family":"Khan","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute, Topi 23460, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2002-8951","authenticated-orcid":false,"given":"Zeashan Hameed","family":"Khan","sequence":"additional","affiliation":[{"name":"Center for Intelligent Manufacturing & Robotics (IRC-IMR), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2526-8436","authenticated-orcid":false,"given":"Adnan","family":"Fazil","sequence":"additional","affiliation":[{"name":"Institute of Avionics & Aeronautics (IAA), Air University (AU), Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4121-3558","authenticated-orcid":false,"given":"Zahid","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan"}]},{"given":"Saeed Mian","family":"Qaisar","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Effat University, Jeddah 21478, Saudi Arabia"},{"name":"CESI LINEACT, 69100 Lyon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6233-598X","authenticated-orcid":false,"given":"Abdul Jabbar","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia"},{"name":"SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12538","DOI":"10.1109\/TCYB.2021.3080121","article-title":"LSCIDMR: Large-Scale Satellite Cloud Image Database for Meteorological Research","volume":"52","author":"Bai","year":"2021","journal-title":"IEEE Trans. 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