Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification
Satellite Image classification provides information about land use land cover (LULC) and this is required in many applications such as Urban planning and environmental monitoring. Recently, deep learning techniques were applied for satellite image classification and achieved higher efficiency. The e...
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| Vydané v: | EURASIP journal on advances in signal processing Ročník 2023; číslo 1; s. 13 - 21 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Cham
Springer International Publishing
01.12.2023
Springer Springer Nature B.V SpringerOpen |
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| ISSN: | 1687-6180, 1687-6172, 1687-6180 |
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| Abstract | Satellite Image classification provides information about land use land cover (LULC) and this is required in many applications such as Urban planning and environmental monitoring. Recently, deep learning techniques were applied for satellite image classification and achieved higher efficiency. The existing techniques in satellite image classification have limitations of overfitting problems due to the convolutional neural network (CNN) model generating more features. This research proposes the optimal guidance-whale optimization algorithm (OG-WOA) technique to select the relevant features and reduce the overfitting problem. The optimal guidance technique increases the exploitation of the search technique by changing the position of the search agent related to the best fitness value. This increase in exploitation helps to select the relevant features and avoid overfitting problems. The input images are normalized and applied to AlexNet–ResNet50 model for feature extraction. The OG-WOA technique is applied in extracted features to select relevant features. Finally, the selected features are processed for classification using Bi-directional long short-term memory (Bi-LSTM). The proposed OG-WOA–Bi-LSTM technique has an accuracy of 97.12% on AID, 99.34% on UCM, and 96.73% on NWPU, SceneNet model has accuracy of 89.58% on AID, and 95.21 on the NWPU dataset. |
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| AbstractList | Satellite Image classification provides information about land use land cover (LULC) and this is required in many applications such as Urban planning and environmental monitoring. Recently, deep learning techniques were applied for satellite image classification and achieved higher efficiency. The existing techniques in satellite image classification have limitations of overfitting problems due to the convolutional neural network (CNN) model generating more features. This research proposes the optimal guidance-whale optimization algorithm (OG-WOA) technique to select the relevant features and reduce the overfitting problem. The optimal guidance technique increases the exploitation of the search technique by changing the position of the search agent related to the best fitness value. This increase in exploitation helps to select the relevant features and avoid overfitting problems. The input images are normalized and applied to AlexNet–ResNet50 model for feature extraction. The OG-WOA technique is applied in extracted features to select relevant features. Finally, the selected features are processed for classification using Bi-directional long short-term memory (Bi-LSTM). The proposed OG-WOA–Bi-LSTM technique has an accuracy of 97.12% on AID, 99.34% on UCM, and 96.73% on NWPU, SceneNet model has accuracy of 89.58% on AID, and 95.21 on the NWPU dataset. Abstract Satellite Image classification provides information about land use land cover (LULC) and this is required in many applications such as Urban planning and environmental monitoring. Recently, deep learning techniques were applied for satellite image classification and achieved higher efficiency. The existing techniques in satellite image classification have limitations of overfitting problems due to the convolutional neural network (CNN) model generating more features. This research proposes the optimal guidance-whale optimization algorithm (OG-WOA) technique to select the relevant features and reduce the overfitting problem. The optimal guidance technique increases the exploitation of the search technique by changing the position of the search agent related to the best fitness value. This increase in exploitation helps to select the relevant features and avoid overfitting problems. The input images are normalized and applied to AlexNet–ResNet50 model for feature extraction. The OG-WOA technique is applied in extracted features to select relevant features. Finally, the selected features are processed for classification using Bi-directional long short-term memory (Bi-LSTM). The proposed OG-WOA–Bi-LSTM technique has an accuracy of 97.12% on AID, 99.34% on UCM, and 96.73% on NWPU, SceneNet model has accuracy of 89.58% on AID, and 95.21 on the NWPU dataset. |
| ArticleNumber | 13 |
| Audience | Academic |
| Author | Babu, J. Ananda Vinaykumar, V. N. Frnda, Jaroslav |
| Author_xml | – sequence: 1 givenname: V. N. surname: Vinaykumar fullname: Vinaykumar, V. N. organization: Department of Information Science and Engineering, Malnad College of Engineering – sequence: 2 givenname: J. Ananda surname: Babu fullname: Babu, J. Ananda organization: Department of Information Science and Engineering, Malnad College of Engineering – sequence: 3 givenname: Jaroslav orcidid: 0000-0001-6065-3087 surname: Frnda fullname: Frnda, Jaroslav email: jaroslav.frnda@uniza.sk organization: Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, University Science Park, University of Zilina |
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| Keywords | Satellite image classification ResNet50 Bi-directional long short-term memory Convolutional neural network Optimal guidance AlexNet Whale optimization algorithm |
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| SubjectTerms | AlexNet Algorithms Analysis Artificial neural networks Bi-directional long short-term memory Cetacea Classification Computational linguistics Convolutional neural network Deep learning Engineering Environmental monitoring Exploitation Feature extraction Image classification Intelligent and Advanced Signal Processing Techniques for Data Fusion in Machine Learning Based Remote Sensing Applications Land cover Land use Language processing Machine learning Mathematical optimization Model accuracy Natural language interfaces Neural networks Optimal guidance Optimization Optimization algorithms Quantum Information Technology ResNet50 Satellite image classification Satellite imagery Signal,Image and Speech Processing Spintronics Urban planning |
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| Title | Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification |
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