Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images
Currently, remote sensing images (RSIs) are often exploited in the explanation of urban and rural areas, change recognition, and other domains. As the majority of RSI is high-resolution and contains wide and varied data, proper interpretation of RSIs is most important. Land use and land cover (LULC)...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10379612/ |
| _version_ | 1827375758480244736 |
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| author | Mohammed Aljebreen Hanan Abdullah Mengash Mohammad Alamgeer Saud S. Alotaibi Ahmed S. Salama Manar Ahmed Hamza |
| author_facet | Mohammed Aljebreen Hanan Abdullah Mengash Mohammad Alamgeer Saud S. Alotaibi Ahmed S. Salama Manar Ahmed Hamza |
| author_sort | Mohammed Aljebreen |
| collection | DOAJ |
| description | Currently, remote sensing images (RSIs) are often exploited in the explanation of urban and rural areas, change recognition, and other domains. As the majority of RSI is high-resolution and contains wide and varied data, proper interpretation of RSIs is most important. Land use and land cover (LULC) classification utilizing deep learning (DL) is a common and efficient manner in remote sensing and geospatial study. It is very important in land planning, environmental monitoring, mapping, and land management. But, one of the recent approaches is problems like vulnerability to noise interference, low classification accuracy, and worse generalization ability. DL approaches, mostly Convolutional Neural Networks (CNNs) revealed impressive performance in image recognition tasks, making them appropriate for LULC classification in RSIs. Therefore, this study introduces a novel Land Use and Land Cover Classification employing the River Formation Dynamics Algorithm with Deep Learning (LULCC-RFDADL) technique on RSIs. The main objective of the LULCC-RFDADL methodology is to recognize the diverse types of LC on RSIs. In the presented LULCC-RFDADL technique, the dense EfficientNet approach is applied for feature extraction. Furthermore, the hyperparameter tuning of the Dense EfficientNet method was implemented using the RFDA technique. For the classification process, the LULCC-RFDADL technique uses the Multi-Scale Convolutional Autoencoder (MSCAE) model. At last, the seeker optimization algorithm (SOA) has been exploited for the parameter choice of the MSCAE system. The achieved outcomes of the LULCC-RFDADL algorithm were examined on benchmark databases. The simulation values show the better result of the LULCC-RFDADL methods with other approaches in terms of different metrics. |
| first_indexed | 2024-03-08T11:57:46Z |
| format | Article |
| id | doaj.art-acb525c16ddc42ecbb974c3d01fd670c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T11:57:46Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-acb525c16ddc42ecbb974c3d01fd670c2024-01-24T00:01:05ZengIEEEIEEE Access2169-35362024-01-0112111471115610.1109/ACCESS.2023.334928510379612Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing ImagesMohammed Aljebreen0Hanan Abdullah Mengash1https://orcid.org/0000-0002-4103-2434Mohammad Alamgeer2https://orcid.org/0000-0003-2575-4493Saud S. Alotaibi3https://orcid.org/0000-0003-1082-513XAhmed S. Salama4https://orcid.org/0000-0002-1066-8261Manar Ahmed Hamza5https://orcid.org/0000-0002-8743-1174Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi ArabiaDepartment of Information Systems, College of Computing and Information Systems, Umm Al-Qura University, Mecca, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaCurrently, remote sensing images (RSIs) are often exploited in the explanation of urban and rural areas, change recognition, and other domains. As the majority of RSI is high-resolution and contains wide and varied data, proper interpretation of RSIs is most important. Land use and land cover (LULC) classification utilizing deep learning (DL) is a common and efficient manner in remote sensing and geospatial study. It is very important in land planning, environmental monitoring, mapping, and land management. But, one of the recent approaches is problems like vulnerability to noise interference, low classification accuracy, and worse generalization ability. DL approaches, mostly Convolutional Neural Networks (CNNs) revealed impressive performance in image recognition tasks, making them appropriate for LULC classification in RSIs. Therefore, this study introduces a novel Land Use and Land Cover Classification employing the River Formation Dynamics Algorithm with Deep Learning (LULCC-RFDADL) technique on RSIs. The main objective of the LULCC-RFDADL methodology is to recognize the diverse types of LC on RSIs. In the presented LULCC-RFDADL technique, the dense EfficientNet approach is applied for feature extraction. Furthermore, the hyperparameter tuning of the Dense EfficientNet method was implemented using the RFDA technique. For the classification process, the LULCC-RFDADL technique uses the Multi-Scale Convolutional Autoencoder (MSCAE) model. At last, the seeker optimization algorithm (SOA) has been exploited for the parameter choice of the MSCAE system. The achieved outcomes of the LULCC-RFDADL algorithm were examined on benchmark databases. The simulation values show the better result of the LULCC-RFDADL methods with other approaches in terms of different metrics.https://ieeexplore.ieee.org/document/10379612/Remote sensing imagesland use classificationland coverdeep learningmetaheuristics |
| spellingShingle | Mohammed Aljebreen Hanan Abdullah Mengash Mohammad Alamgeer Saud S. Alotaibi Ahmed S. Salama Manar Ahmed Hamza Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images IEEE Access Remote sensing images land use classification land cover deep learning metaheuristics |
| title | Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images |
| title_full | Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images |
| title_fullStr | Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images |
| title_full_unstemmed | Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images |
| title_short | Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images |
| title_sort | land use and land cover classification using river formation dynamics algorithm with deep learning on remote sensing images |
| topic | Remote sensing images land use classification land cover deep learning metaheuristics |
| url | https://ieeexplore.ieee.org/document/10379612/ |
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