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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Bibliographic Details
Published in:IEEE access Vol. 12; p. 1
Main Authors: Aljebreen, Mohammed, Mengash, Hanan Abdullah, Alamgeer, Mohammad, Alotaibi, Saud S., Salama, Ahmed S., Hamza, Manar Ahmed
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3349285