Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques
Article
Karadal, Can Haktan, Kaya, M. Cagri, Tuncer, Turker, Dogan, Sengul and Acharya, U. Rajendra. 2021. "Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques." Expert Systems with Applications. 185. https://doi.org/10.1016/j.eswa.2021.115659
Article Title | Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques |
---|---|
ERA Journal ID | 17852 |
Article Category | Article |
Authors | Karadal, Can Haktan, Kaya, M. Cagri, Tuncer, Turker, Dogan, Sengul and Acharya, U. Rajendra |
Journal Title | Expert Systems with Applications |
Journal Citation | 185 |
Article Number | 115659 |
Number of Pages | 15 |
Year | 2021 |
Publisher | Elsevier |
ISSN | 0957-4174 |
1873-6793 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.eswa.2021.115659 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0957417421010502 |
Abstract | Automated classification of remote sensing images is one of the complex issues in robotics and machine learning fields. Many models have been proposed for remote sensing image classification (RSIC) to obtain high classification performance. The objective of this study are twofold. First, to create a new space object image collection as such a dataset is not currently available. Second, propose a novel RSIC model to yield highest classification performance using our newly created dataset. Our presented automated classification model consists of multilevel deep feature generation, iterative feature selection, and classification steps. The features are extracted from the images using pre-trained MobileNetV2 and discrete wavelet transform (DWT) methods. The combination of DWT and MobileNetV2 generates large number of features. Then, iterative neighborhood component analysis (INCA) is used to select the best features. Finally, selected features are fed to support vector machine (SVM) for automated classification. The presented model is validated using two RSIC datasets: UC-Merced, and newly created space object images (publicly available at: http://web.firat.edu.tr/turkertuncer/space_object.rar). The developed model has obtained an accuracy of 98.10% and 95.95% using UC-Merced, and newly generated space object image datasets, respectively with 10-fold cross-validation strategy. It can be concluded from the results that, the presented RSIC model is accurate and ready for real-world applications. |
Keywords | INCA; MobilNetV2; Multilevel feature generation ; Remote sensing image classification |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Firat University, Turkey |
Middle East Technical University, Turkey | |
Ardahan University, Turkiye | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
Permalink -
https://research.usq.edu.au/item/z1vv0/automated-classification-of-remote-sensing-images-using-multileveled-mobilenetv2-and-dwt-techniques
63
total views0
total downloads0
views this month0
downloads this month