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The PCA- and BHHO-derived features are merged into a supplemented hybrid feature set, then evaluated with logistic regression for supervised classification. Feature set combinations are tested to compare the hybrid strategy against logistic regression inputs, achieving up to 99.2% accuracy and statistically significant performance gains.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/presentations/","Presentations",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/hybrid-deep-learning-and-optimized-feature-selection-for-oil-spill-detection-in-satellite-images/563123/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/hybrid-deep-learning-and-optimized-feature-selection-for-oil-spill-detection-in-satellite-images/563123.png","ImageObject",442,249,{"name":42,"@type":43},"GGG","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-24","2026-10-12",true,{"@type":52,"interactionType":53,"userInteractionCount":30},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What input data and main learning components are used for oil spill detection?","Question",{"text":62,"@type":63},"The approach uses Synthetic Aperture Radar (SAR) imagery combined with MobileNet-based deep transfer learning to extract deep features for subsequent classification.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"How are optimal features selected in the proposed hybrid method?",{"text":67,"@type":63},"Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) are applied to identify subsets of optimal features from the feature dataset extracted by MobileNet.",{"name":69,"@type":60,"acceptedAnswer":70},"What classification model achieved the highest reported accuracy?",{"text":71,"@type":63},"Logistic regression supervised machine learning classification achieved the highest oil spill detection accuracy, reaching 99.2% when using the integrated hybrid feature inputs selected by PCA and BHHO.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},563123,1792492715,{"code":4,"msg":81,"data":82},"success",[83,87,92,97,102,107,112,117,122],{"id":84,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":85,"slug":86},11,90,"presentations",{"id":88,"doc_module":22,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},12,"Resumes",80,"resumes",{"id":93,"doc_module":22,"doc_module_name":25,"category_name":94,"show_sort_weight":95,"slug":96},14,"Invoices",70,"invoices",{"id":98,"doc_module":22,"doc_module_name":25,"category_name":99,"show_sort_weight":100,"slug":101},15,"Posters",60,"posters",{"id":103,"doc_module":22,"doc_module_name":25,"category_name":104,"show_sort_weight":105,"slug":106},16,"Social Media",50,"social-media",{"id":108,"doc_module":22,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},17,"Forms",40,"forms",{"id":113,"doc_module":22,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},18,"Letters",30,"letters",{"id":118,"doc_module":22,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},21,"Paper Templates",5,"papers-templates",{"id":123,"doc_module":22,"doc_module_name":25,"category_name":124,"show_sort_weight":4,"slug":125},158,"General","general-158",{"code":4,"msg":81,"data":127},{"doc_id":78,"user_id":128,"nickname":42,"user_avatar":129,"doc_module":22,"category_id":84,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":30,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":118,"language":135,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":12,"update_tm":139,"read_time":140},962093532725,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Tech Science Press  \nDoi:10.32604/cmc.2025.063363  \nARTICLE  \nHybrid Deep Learning and Optimized Feature Selection for Oil Spill Detection in Satellite Images  \nGhada Atteia1, * , Mohammed Dabboor2 , Konstantinos Karantzalos3 and Maali Alabdulhafith1  \n1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia  \n2 Science and Technology Branch, Environment and Climate Change Canada, Dorval, QC H9P 1J3, Canada  \n3 Laboratory of Remote Sensing, Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Athens, 10682, Greece  \n*[Corresponding Author: Ghada Atteia. Email: geatteiaallah@pnu.edu.sa](Corresponding Author: Ghada Atteia. Email: geatteiaallah@pnu.edu.sa)[ ](Corresponding Author: Ghada Atteia. Email: geatteiaallah@pnu.edu.sa)[Received: 13 January 2025](Received: 13 January 2025); [Accepted: 29 April 2025](Accepted: 29 April 2025); [Published: 09 June 2025](Published: 09 June 2025)  \nABSTRACT: This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of optimal features from the entire feature dataset extracted by MobileNet. A supplemented hybrid feature set was constructed from the PCA and BHHO-generated features. It was used as input for oil spill detection using the logistic regression supervised machine learning classification algorithm. Several feature set combinations were implemented to test the classification performance of the logistic regression classifier in comparison to that of the proposed hybrid feature set. Results indicate that the highest oil spill detection accuracy of 99.2% has been achieved using the logistic regression classification algorithm, with integrated feature input from subsets identified using the PCA and the BHHO feature selection techniques. The proposed method yielded a statistically significant improvement in the classification performance of the used machine learning model. The significance of our study lies in its unique integration of deep learning with optimized feature selection, unlike other published studies, to enhance oil spill detection accuracy.  \nKEYWORDS: Oil spill; machine learning; deep learning; classification; metaheuristic optimization  \n1 Introduction  \nOil spill detection using Synthetic Aperture Radar (SAR) imagery and deep learning techniques has emerged as a promising solution for monitoring and mitigating environmental hazards. SAR imagery provides all-weather and day-and-night capabilities, making it an invaluable tool for detecting oil spills in marine environments. Oil spills are detected in SAR imagery by identifying areas of reduced backscatter, where the oil dampens surface capillary waves, creating smooth, dark patches on the water surface. These dark features contrast with the surrounding sea surface, allowing analysts or automated algorithms to flag potential oil contamination. Artificial intelligence (AI) tools have demonstrated significant effectiveness in analyzing satellite data for various environmental applications, including soil moisture retrieval,  \nCopyright © 2025 The Authors. Published by Tech Science Press.  \nThis work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction i","cbCaiqkFCy0gTp8H","https://ap.wps.com/l/cbCaiqkFCy0gTp8H","pdf",1339601,"English","# Abstract\n## Keywords\n## 1 Introduction","[{\"question\":\"What input data and main learning components are used for oil spill detection?\",\"answer\":\"The approach uses Synthetic Aperture Radar (SAR) imagery combined with MobileNet-based deep transfer learning to extract deep features for subsequent classification.\"},{\"question\":\"How are optimal features selected in the proposed hybrid method?\",\"answer\":\"Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) are applied to identify subsets of optimal features from the feature dataset extracted by MobileNet.\"},{\"question\":\"What classification model achieved the highest reported accuracy?\",\"answer\":\"Logistic regression supervised machine learning classification achieved the highest oil spill detection accuracy, reaching 99.2% when using the integrated hybrid feature inputs selected by PCA and BHHO.\"}]","Hybrid Deep Learning and Optimized Feature Selection for Oil Spill Detection in Satellite Images | PDF",1791811625,7]