{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:52:34Z","timestamp":1771519954016,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,12]],"date-time":"2023-02-12T00:00:00Z","timestamp":1676160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education in Saudi Arabia","award":["MoE-IF-UJ-R-22-0708-1"],"award-info":[{"award-number":["MoE-IF-UJ-R-22-0708-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing is a long-distance measuring technology that obtains data about a phenomenon or an object. Remote sensing technology plays a crucial role in several domains, such as weather forecasts, resource surveys, disaster evaluation and environment protection. The application of remote-sensing images (RSIs) is extensive in some specific domains, such as national security and business secrets. Simple multimedia distribution techniques and the development of the Internet make the content security of RSIs a significant problem for both engineers and scientists. In this background, RSI classification using deep learning (DL) models becomes essential. Therefore, the current research article develops a block-scrambling-based encryption with privacy preserving optimal deep-learning-driven classification (BSBE-PPODLC) technique for the classification of RSIs. The presented BSBE-PPODLC technique follows a two-stage process, i.e., image encryption and classification. Initially, the RSI encryption process takes place based on a BSBE approach. In the second stage, the image classification process is performed, and it encompasses multiple phases, such as densely connected network (DenseNet) feature extraction, extreme gradient boosting (XGBoost) classifier and artificial gorilla troops optimizer (AGTO)-based hyperparameter tuning. The proposed BSBE-PPODLC technique was simulated using the RSI dataset, and the outcomes were assessed under different aspects. The outcomes confirmed that the presented BSBE-PPODLC approach accomplished improved performance compared to the existing models.<\/jats:p>","DOI":"10.3390\/rs15041022","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T01:48:56Z","timestamp":1676252936000},"page":"1022","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Block-Scrambling-Based Encryption with Deep-Learning-Driven Remote Sensing Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7332-3773","authenticated-orcid":false,"given":"Faisal S.","family":"Alsubaei","sequence":"first","affiliation":[{"name":"Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia"}]},{"given":"Amani A.","family":"Alneil","sequence":"additional","affiliation":[{"name":"Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj 16278, Saudi Arabia"}]},{"given":"Abdullah","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Research Centre, Future University in Egypt, New Cairo 11845, Egypt"}]},{"given":"Anwer","family":"Mustafa Hilal","sequence":"additional","affiliation":[{"name":"Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj 16278, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chouragade, P.M., and Ambhore, P.B. 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