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Stefan cel Mare
University of Suceava
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Computer Science
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  4/2023 - 4

Transfer Learning Based Convolutional Neural Network for Classification of Remote Sensing Images

RAMASAMY, M. P. See more information about RAMASAMY, M. P. on SCOPUS See more information about RAMASAMY, M. P. on IEEExplore See more information about RAMASAMY, M. P. on Web of Science, KRISHNASAMY, V. See more information about  KRISHNASAMY, V. on SCOPUS See more information about  KRISHNASAMY, V. on SCOPUS See more information about KRISHNASAMY, V. on Web of Science, RAMAPACKIAM, S. S. K. See more information about RAMAPACKIAM, S. S. K. on SCOPUS See more information about RAMAPACKIAM, S. S. K. on SCOPUS See more information about RAMAPACKIAM, S. S. K. on Web of Science
 
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Download PDF pdficon (1,946 KB) | Citation | Downloads: 697 | Views: 515

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
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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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References Weight

Web of Science® Citations for all references: 4,126 TCR
SCOPUS® Citations for all references: 4,890 TCR

Web of Science® Average Citations per reference: 106 ACR
SCOPUS® Average Citations per reference: 125 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-23 14:50 in 229 seconds.




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