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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Veröffentlicht in:	IEEE access Jg. 12; S. 1
Hauptverfasser:	Aljebreen, Mohammed, Mengash, Hanan Abdullah, Alamgeer, Mohammad, Alotaibi, Saud S., Salama, Ahmed S., Hamza, Manar Ahmed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Classification Classification algorithms Deep learning Environmental monitoring Feature extraction Heuristic algorithms Land cover Land management Land use Land use classification Land use planning Machine learning Metaheuristics Remote sensing Remote sensing images Satellite images
ISSN:	2169-3536, 2169-3536
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Abstract	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.
AbstractList	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.
Author	Hamza, Manar Ahmed Alamgeer, Mohammad Aljebreen, Mohammed Mengash, Hanan Abdullah Alotaibi, Saud S. Salama, Ahmed S.
Author_xml	– sequence: 1 givenname: Mohammed surname: Aljebreen fullname: Aljebreen, Mohammed organization: Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh, Saudi Arabia – sequence: 2 givenname: Hanan Abdullah orcidid: 0000-0002-4103-2434 surname: Mengash fullname: Mengash, Hanan Abdullah organization: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia – sequence: 3 givenname: Mohammad orcidid: 0000-0003-2575-4493 surname: Alamgeer fullname: Alamgeer, Mohammad organization: Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia – sequence: 4 givenname: Saud S. orcidid: 0000-0003-1082-513X surname: Alotaibi fullname: Alotaibi, Saud S. organization: Department of Information Systems, College of Computing and Information Systems, Umm Al-Qura University, Saudi Arabia – sequence: 5 givenname: Ahmed S. orcidid: 0000-0002-1066-8261 surname: Salama fullname: Salama, Ahmed S. organization: Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, Egypt – sequence: 6 givenname: Manar Ahmed orcidid: 0000-0002-8743-1174 surname: Hamza fullname: Hamza, Manar Ahmed organization: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
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SubjectTerms	Algorithms Artificial neural networks Classification Classification algorithms Deep learning Environmental monitoring Feature extraction Heuristic algorithms Land cover Land management Land use Land use classification Land use planning Machine learning Metaheuristics Remote sensing Remote sensing images Satellite images
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Title	Land Use and Land Cover Classification using River Formation Dynamics Algorithm with Deep Learning on Remote Sensing Images
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