Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification
Oceans and seas cover more than 70% of the Earth's surface. If compared with the land mass there are a lot of unexplored locations, a wealth of natural resources, and diverse ocean creatures that are inaccessible to us humans. Underwater rovers and vehicles play a vital role in...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10815598/ |
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| author | Wad Ghaban Jawad Ahmad Ali Akbar Siddique Mohammad S. Alshehri Anila Saghir Faisal Saeed Baraq Ghaleb Mujeeb Ur Rehman |
| author_facet | Wad Ghaban Jawad Ahmad Ali Akbar Siddique Mohammad S. Alshehri Anila Saghir Faisal Saeed Baraq Ghaleb Mujeeb Ur Rehman |
| author_sort | Wad Ghaban |
| collection | DOAJ |
| description | Oceans and seas cover more than 70% of the Earth's surface. If compared with the land mass there are a lot of unexplored locations, a wealth of natural resources, and diverse ocean creatures that are inaccessible to us humans. Underwater rovers and vehicles play a vital role in discovering these resources, yet limited visibility in deep waters and technological constraints impede underwater exploration. To address these issues, advanced image super-resolution and enhancement techniques are crucial for reliable resource identification, species recognition, and underwater ecosystem study. This will ultimately bridge the current gap in environmental monitoring by facilitating resource tracking and underwater waste assessment. This article proposes a novel multistage fusion algorithm for underwater image super-resolution, designed to enhance the quality and spatial resolution of low-resolution underwater images toward a more accurate object characterization. The effectiveness of the proposed super-resolution technique is demonstrated using multiple performance metrics including accuracy, <italic>f</italic>1-score, recall, and precision. By enhancing the spatial resolution of underwater images, our approach meets the increasing demand for detailed and accurate information in underwater earth observation applications. |
| format | Article |
| id | doaj-art-ecdd52d3cbbe4bdc85fe12434fcbd087 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-ecdd52d3cbbe4bdc85fe12434fcbd0872025-01-21T00:00:25ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183640365310.1109/JSTARS.2024.352220210815598Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and ClassificationWad Ghaban0https://orcid.org/0000-0003-0564-4377Jawad Ahmad1https://orcid.org/0000-0001-6289-8248Ali Akbar Siddique2https://orcid.org/0000-0002-9641-7067Mohammad S. Alshehri3https://orcid.org/0000-0001-9471-7720Anila Saghir4https://orcid.org/0000-0001-6688-5276Faisal Saeed5https://orcid.org/0000-0002-2822-1708Baraq Ghaleb6https://orcid.org/0000-0002-8361-0634Mujeeb Ur Rehman7https://orcid.org/0000-0002-8154-6560Applied College, University of Tabuk, Tabuk, Saudi ArabiaCybersecurity Center, Prince Mohammad Bin Fahd University, Al Khobar, Saudi ArabiaDepartment of Computer Science, Iqra University, Karachi, PakistanDepartment of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi ArabiaDepartment of Telecommunication Engineering, Sir Syed University of Engineering and Technology, Karachi, PakistanCollege of Computing, Birmingham City University, Birmingham, U.K.School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, U.K.Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, Leicester, U.K.Oceans and seas cover more than 70% of the Earth's surface. If compared with the land mass there are a lot of unexplored locations, a wealth of natural resources, and diverse ocean creatures that are inaccessible to us humans. Underwater rovers and vehicles play a vital role in discovering these resources, yet limited visibility in deep waters and technological constraints impede underwater exploration. To address these issues, advanced image super-resolution and enhancement techniques are crucial for reliable resource identification, species recognition, and underwater ecosystem study. This will ultimately bridge the current gap in environmental monitoring by facilitating resource tracking and underwater waste assessment. This article proposes a novel multistage fusion algorithm for underwater image super-resolution, designed to enhance the quality and spatial resolution of low-resolution underwater images toward a more accurate object characterization. The effectiveness of the proposed super-resolution technique is demonstrated using multiple performance metrics including accuracy, <italic>f</italic>1-score, recall, and precision. By enhancing the spatial resolution of underwater images, our approach meets the increasing demand for detailed and accurate information in underwater earth observation applications.https://ieeexplore.ieee.org/document/10815598/Image classificationimage enhancementimage processingimage recognitionremote sensing |
| spellingShingle | Wad Ghaban Jawad Ahmad Ali Akbar Siddique Mohammad S. Alshehri Anila Saghir Faisal Saeed Baraq Ghaleb Mujeeb Ur Rehman Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Image classification image enhancement image processing image recognition remote sensing |
| title | Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification |
| title_full | Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification |
| title_fullStr | Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification |
| title_full_unstemmed | Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification |
| title_short | Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification |
| title_sort | sustainable environmental monitoring multistage fusion algorithm for remotely sensed underwater super resolution image enhancement and classification |
| topic | Image classification image enhancement image processing image recognition remote sensing |
| url | https://ieeexplore.ieee.org/document/10815598/ |
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