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Transfer Learning Based Convolutional Neural Network for Classification of Remote Sensing ImagesRAMASAMY, M. P.   , KRISHNASAMY, V. , RAMAPACKIAM, S. S. K. |
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Author keywords
remote sensing, transfer learning, classification, convolutional neural network, deep learning
References keywords
sensing(44), remote(44), classification(29), deep(17), learning(16), neural(15), convolutional(13), scene(12), network(12), land(10)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2023-11-30
Volume 23, Issue 4, Year 2023, On page(s): 31 - 40
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.04004
Web of Science Accession Number: 001147490000002
SCOPUS ID: 85182194217
Abstract
Classification of Land cover Remote sensing images find a lot of applications including regional planning, natural resources conservation and management, agricultural monitoring etc., Presently, Convolutional Neural Networks (CNN) which are deep learning based methods are successfully employed for classification problems due to its flexible architecture and potentiality to learn new features from raw data. The motivation of the work is to implement a robust deep learning architecture for the classification of remote sensing images using a transfer learning approach. Deep learning requires a large amount of time if the training is initiated from scratch. Transfer learning overcomes this drawback by using pre-trained models efficiently. In the proposed work, a transfer learning based Convolutional Neural Network is used for the classification of remote sensing images. Three popular pre-trained models VGG16, ResNet50 and Densenet121 are used for feature extraction and a fully connected layer is used for classification. Results indicate that the transfer learning based Convolutional Neural Network with data augmentation and optimization of model parameters gives better performance compared to training from scratch for the classification of remote sensing images. Experimental results indicate that an improved accuracy of 95.88 percent is obtained for the proposed Transfer learning method for the remote sensing dataset of UC-Merced. |
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[1] Urbanscape-Net: A Spatial and Self-Attention Guided Deep Neural Network with Multi Scale Feature Extraction for Urban Land-Use Classification, Chatterjee, Abhiroop, Ghosh, Susmita, Ghosh, Ashish, Ientilucci, Emmett, IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, ISBN 979-8-3503-6032-5, 2024.
Digital Object Identifier: 10.1109/IGARSS53475.2024.10640965 [CrossRef]
[2] Machine Learning-Based Crack Detection Methods in Ancient Buildings, Fang, Tianke, Hui, Zhenxing, P.Rey, William, Yang, Aihua, Liu, Bin, He, Yuanrong, Proceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, ISBN 9798400717147, 2024.
Digital Object Identifier: 10.1145/3675417.3675564 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
