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Research Article
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Year 2025, Volume: 9 Issue: 1, 1 - 14, 17.06.2025

Meriç Genç , Yıldıray Yalman

https://doi.org/10.38088/jise.1527548

Abstract

References

  • 1. Chen, H., Zhao, J., Gao, T., and Chen, W. (2018). Fast Airplane Detection with Hierarchical Structure in Large Scene Remote Sensing Images at High Spatial Resolution. IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 22-27, 4846–4849.
  • 2. Luo, Q., and Shi, Z. (2016). Airplane detection in remote sensing images based on Object Proposal. International Geoscience and Remote Sensing Symposium (IGARSS), 1388–1391.
  • 3. Fukushima, K. N. (1980). A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., 36(4): 193–202.
  • 4. Hubel, D. H. and Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. J. Physiol., 195(1):215–243.
  • 5. Le Cun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc. IEEE, 8(11): 2278–2324.
  • 6. Le Cun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521: 436–444.
  • 7. Le Cun, Y., (1989). Handwritten digit recognition: applications of neural network chips and automatic learning. IEEE Commun. Mag., 27(11): 41–46.
  • 8. Cirean, D., Meier, U., and Schmidhuber, J. (2012). Multi-column Deep Neural Networks for Image Classification., Feb. 2012.
  • 9. Cirean, D. C., Meier, U., Masci, J., and Gambardella, L. M. (2012). Flexible, High Performance Convolutional Neural Networks for Image Classification. in Proceedings of the 22nd International Joint Conference on Artificial Intelligence, 1237–1242
  • 10. Farfade, S. S., Saberian, M., and Li, L.-J. (2015). Multiview Face Detection Using Deep Convolutional Neural Networks. Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. 643–650.
  • 11. Grefenstette, E., Blunsom, P., de Freitas, N., and Hermann, K. M. (2014). A Deep Architecture for Semantic Parsing. The Semantic Parsing Workshop, April.
  • 12. Shen, Y., He, X., Gao, J., Deng, L., and Mesnil, G. (2014). Learning semantic representations using convolutional neural networks for web search. 23rd International Conference on World Wide Web - WWW ’14 Companion, 373–374.
  • 13. Kalchbrenner, N., Grefenstette, E., and Blunsom, P. (2014). A Convolutional Neural Network for Modelling Sentences. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 655-665.
  • 14. Kim, Y. (2014). Convolutional neural networks for sentence classification. Conference on Empirical Methods in Natural Language Processing (EMNLP). 1746-1751.
  • 15. Collobert, R. and Weston, J. (2008). A unified architecture for natural language processing: Deep neural networks with multitask learning. 25th International conference on Machine learning. 20(1):160–167.
  • 16. Wallach, I., Dzamba, M., and Heifets, A. (2015). AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery.
  • 17. Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision. 60(2): 91–110.
  • 18. Dalal, N., Triggs, B. (2005). Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA. 886–893.
  • 19. Yang, J., Yu, K., Gong, Y., Huang, T.S. (2009). Linear spatial pyramid matching using sparse coding for image classification, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009). 1794–1801.
  • 20. Yu, K., Zhang, T. and Gong, Y. (2009) Nonlinear learning using local coordinate coding, Advances in Neural Information Processing Systems, Vancouver, Canada, 2223–2231.

Aircraft Recognition Based on CNN Using Satellite Images

Year 2025, Volume: 9 Issue: 1, 1 - 14, 17.06.2025

Meriç Genç , Yıldıray Yalman

https://doi.org/10.38088/jise.1527548

Abstract

This study examines aircraft recognition using Convolutional Neural Networks (CNN) with satellite-derived image data. The research traces the evolution of deep learning, emphasizing the importance of multi-layer neural networks in addressing the limitations of artificial intelligence. The study exclusively utilizes the MTARSI dataset and employs VGG16 and VGG19 models. The motivation stems from the critical role of aircraft recognition in civil aviation, military security, and emergency interventions. The aim of this study is to develop aircraft recognition systems using CNNs. Performance analysis of the VGG16 and VGG19 models in military aircraft recognition tasks demonstrates the superior accuracy of VGG19, with success rates of 82.67% for VGG16 and 89.29% for VGG19. These results highlight the importance of advanced models like VGG19 in the future development of military aircraft recognition systems. The VGG16 and VGG19 models used in this study outperformed other traditional methods. Based on the above analysis, it is evident that the VGG16 and VGG19 models demonstrated higher success rates compared to other traditional methods. The VGG16 model achieved an accuracy of 82.67%, while the VGG19 model achieved an accuracy of 89.29%. These findings underscore the importance of utilizing advanced models like VGG19 in the future development of military aircraft recognition systems, highlighting their significant advantage over traditional approaches in this domain.

Keywords

VGG16 VGG19 Convolutional Neural Networks Aircraft Recognition Visual Recognition

References

  • 1. Chen, H., Zhao, J., Gao, T., and Chen, W. (2018). Fast Airplane Detection with Hierarchical Structure in Large Scene Remote Sensing Images at High Spatial Resolution. IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 22-27, 4846–4849.
  • 2. Luo, Q., and Shi, Z. (2016). Airplane detection in remote sensing images based on Object Proposal. International Geoscience and Remote Sensing Symposium (IGARSS), 1388–1391.
  • 3. Fukushima, K. N. (1980). A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., 36(4): 193–202.
  • 4. Hubel, D. H. and Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. J. Physiol., 195(1):215–243.
  • 5. Le Cun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc. IEEE, 8(11): 2278–2324.
  • 6. Le Cun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521: 436–444.
  • 7. Le Cun, Y., (1989). Handwritten digit recognition: applications of neural network chips and automatic learning. IEEE Commun. Mag., 27(11): 41–46.
  • 8. Cirean, D., Meier, U., and Schmidhuber, J. (2012). Multi-column Deep Neural Networks for Image Classification., Feb. 2012.
  • 9. Cirean, D. C., Meier, U., Masci, J., and Gambardella, L. M. (2012). Flexible, High Performance Convolutional Neural Networks for Image Classification. in Proceedings of the 22nd International Joint Conference on Artificial Intelligence, 1237–1242
  • 10. Farfade, S. S., Saberian, M., and Li, L.-J. (2015). Multiview Face Detection Using Deep Convolutional Neural Networks. Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. 643–650.
  • 11. Grefenstette, E., Blunsom, P., de Freitas, N., and Hermann, K. M. (2014). A Deep Architecture for Semantic Parsing. The Semantic Parsing Workshop, April.
  • 12. Shen, Y., He, X., Gao, J., Deng, L., and Mesnil, G. (2014). Learning semantic representations using convolutional neural networks for web search. 23rd International Conference on World Wide Web - WWW ’14 Companion, 373–374.
  • 13. Kalchbrenner, N., Grefenstette, E., and Blunsom, P. (2014). A Convolutional Neural Network for Modelling Sentences. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 655-665.
  • 14. Kim, Y. (2014). Convolutional neural networks for sentence classification. Conference on Empirical Methods in Natural Language Processing (EMNLP). 1746-1751.
  • 15. Collobert, R. and Weston, J. (2008). A unified architecture for natural language processing: Deep neural networks with multitask learning. 25th International conference on Machine learning. 20(1):160–167.
  • 16. Wallach, I., Dzamba, M., and Heifets, A. (2015). AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery.
  • 17. Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision. 60(2): 91–110.
  • 18. Dalal, N., Triggs, B. (2005). Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA. 886–893.
  • 19. Yang, J., Yu, K., Gong, Y., Huang, T.S. (2009). Linear spatial pyramid matching using sparse coding for image classification, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009). 1794–1801.
  • 20. Yu, K., Zhang, T. and Gong, Y. (2009) Nonlinear learning using local coordinate coding, Advances in Neural Information Processing Systems, Vancouver, Canada, 2223–2231.
There are 20 citations in total.

Details

Primary Language English
Subjects Computer Vision
Journal Section Research Articles
Authors

Meriç Genç 0009-0006-3547-5729

Yıldıray Yalman 0000-0002-2313-4525

Early Pub Date April 10, 2025
Publication Date June 17, 2025
Submission Date August 3, 2024
Acceptance Date November 24, 2024
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Genç, M., & Yalman, Y. (2025). Aircraft Recognition Based on CNN Using Satellite Images. Journal of Innovative Science and Engineering, 9(1), 1-14. https://doi.org/10.38088/jise.1527548
AMA Genç M, Yalman Y. Aircraft Recognition Based on CNN Using Satellite Images. JISE. June 2025;9(1):1-14. doi:10.38088/jise.1527548
Chicago Genç, Meriç, and Yıldıray Yalman. “Aircraft Recognition Based on CNN Using Satellite Images”. Journal of Innovative Science and Engineering 9, no. 1 (June 2025): 1-14. https://doi.org/10.38088/jise.1527548.
EndNote Genç M, Yalman Y (June 1, 2025) Aircraft Recognition Based on CNN Using Satellite Images. Journal of Innovative Science and Engineering 9 1 1–14.
IEEE M. Genç and Y. Yalman, “Aircraft Recognition Based on CNN Using Satellite Images”, JISE, vol. 9, no. 1, pp. 1–14, 2025, doi: 10.38088/jise.1527548.
ISNAD Genç, Meriç - Yalman, Yıldıray. “Aircraft Recognition Based on CNN Using Satellite Images”. Journal of Innovative Science and Engineering 9/1 (June2025), 1-14. https://doi.org/10.38088/jise.1527548.
JAMA Genç M, Yalman Y. Aircraft Recognition Based on CNN Using Satellite Images. JISE. 2025;9:1–14.
MLA Genç, Meriç and Yıldıray Yalman. “Aircraft Recognition Based on CNN Using Satellite Images”. Journal of Innovative Science and Engineering, vol. 9, no. 1, 2025, pp. 1-14, doi:10.38088/jise.1527548.
Vancouver Genç M, Yalman Y. Aircraft Recognition Based on CNN Using Satellite Images. JISE. 2025;9(1):1-14.
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