International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

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DOI Prefix: 10.47001/IRJIET

Classification of Desert Areas Using Remote Sensing Images and Hybrid Machine Learning

Rafal Nazar Younes AL-TahanDepartment of Computer Science, College of Computer Science and Mathematics, University of Mosul, Mosul, IraqRuba Talal IbrahimDepartment of Computer Science, College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq

Vol 8 No 11 (2024): Volume 8, Issue 11, November 2024 | Pages: 6-11

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 04-11-2024

doi Logo doi.org/10.47001/IRJIET/2024.811002

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Abstract

In order to control desertification, it must first be discovered and classified, and then sustainable agriculture must be promoted to avoid it. It is identified using topographic features of desert areas that vary over time. Therefore, it is necessary to classify and identify desert areas with high accuracy using satellite remote sensing images (SRSI). In this paper, a hybridization was made between Exception transfer learning method, which was used to extract features, and state of art machine learning LightGBM method, which was used to classify (SRSI) dataset approved by Kaggle website. Several pre-processing was also performed on the dataset, such as image cropping to get the important features, as well as performing the data augmentation process to increase the amount of data and make it in different positions. After making a comparison with traditional and previous methods, such as Naive Bayes and K-Nearest Neighbors (KNN), the experiment results showed that LightGBM outperformed them and achieved a high accuracy of 99% and AUC of 100%.

Keywords

LightGBM, desert, SRSI, Classification and Exception transfer learning


Citation of this Article

Rafal Nazar Younes AL-Tahan, & Ruba Talal Ibrahim (Ph. D). (2024). Classification of Desert Areas Using Remote Sensing Images and Hybrid Machine Learning. International Research Journal of Innovations in Engineering and Technology - IRJIET, 8(11), 6-11. Article DOI https://doi.org/10.47001/IRJIET/2024.811002

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. D. Yadav, K. Kapoor, A. K. Yadav, M. Kumar, A. Jain, and J. Morato, “Satellite image classification using deep learning approach,” Earth Sci. Informatics, vol. 17, no. 3, pp. 2495–2508, 2024, doi: 10.1007/s12145-024-01301-x.
  2. G. Di, Haibin and Wang, Zhen and AlRegib, “Real-time seismic-image interpretation via deconvolutional neural network,” SEG Int. Expo. Annu. Meet., 2018.
  3. V. Moosavi, “Application of Taguchi method to satellite image fusion for object-oriented,” Geosci. J., vol. 18, pp. 45–59, 2013, doi: 10.1007/s12303-013-0044-9.
  4. S. Réjichi and F. Chaabane, “Feature extraction using PCA for VHR satellite image time series spatio-temporal classification,” 2015 IEEE Int. Geosci. Remote Sens. Symp., pp. 485–488, 2015.
  5. B. Liu, Y. Li, G. Li, and A. Liu, “A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill,” ISPRS Int. J. Geo-Information, vol. 8, no. 4, p. 160, 2019, doi: 10.3390/ijgi8040160.
  6. M. A. Haq, G. Rahaman, P. Baral, and A. Ghosh, “Deep Learning Based Supervised Image Classification Using UAV Images for Forest Areas Classification,” J. Indian Soc. Remote Sens., vol. 49, no. 3, pp. 601–606, 2021, doi: 10.1007/s12524-020-01231-3.
  7. Y. Qing and W. Liu, “Hyperspectral image classification based on multi-scale residual network with attention mechanism,” Remote Sens., vol. 13, no. 3, pp. 1–18, 2021, doi: 10.3390/rs13030335.
  8. M. M. A. Humayra Ferdous , Tasnim Siraj, Shifat Jahan Setu and  and M. A. Rahman, “Machine Learning Approach Towards Satellite Image Classification,” Proc. Int. Conf. Trends Comput. Cogn. Eng., pp. 627--637, 2021, doi: https://doi.org/10.1007/978-981-33-4673-4_51.
  9. M. Balajee, J and Saleem Durai, “Drought prediction and analysis of water level based on satellite images using deep convolutional neural network,” Int. J. Speech Technol., vol. 25, no. 3, pp. 615--623, 2022, doi: https://doi.org/10.1007/s10772-022-09995-4.
  10. H. Li, C. Zhang, S. Zhang, X. Ding, and P. M. Atkinson, “Iterative Deep Learning ( IDL ) for agricultural landscape classification using fine spatial resolution remotely sensed imagery,” Int. J. Appl. Earth Obs. Geoinf., vol. 102, no. July, p. 102437, 2021, doi: 10.1016/j.jag.2021.102437.
  11. C. M. Gevaert and M. Belgiu, “Assessing the generalization capability of deep learning networks for aerial image classification using landscape metrics,” Int. J. Appl. Earth Obs. Geoinf., vol. 114, no. October, p. 103054, 2022, doi: 10.1016/j.jag.2022.103054.
  12. S. Chaudhari, V. Sardar, and P. Ghosh, “Drought classification and prediction with satellite image-based indices using variants of deep learning models,” Int. J. Inf. Technol., vol. 15, no. 7, pp. 3463–3472, 2023, doi: 10.1007/s41870-023-01379-4.
  13. H. En-Nagre, Khalid and Aqnouy, Mourad and Ouarka, Ayoub and Naqvi, Syed Ali Asad and Bouizrou, Ismail and El Messari, Jamal Eddine Stitou and Tariq, Aqil and Soufan, Walid and Li, Wenzhao and El-Askary, “Assessment and prediction of meteorological drought using machine learning algorithms and climate data,” Clim. Risk Manag., vol. 45, p. 100630, 2024, doi: https://doi.org/10.1016/j.crm.2024.100630.
  14. F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 1800–1807, 2017, doi: 10.1109/CVPR.2017.195.
  15. V. Mahalakshmi, P. Shenbagavalli, S. Raguvaran, V. Rajakumareswaran, and E. Sivaraman, “Twitter sentiment analysis using conditional generative adversarial network,” Int. J. Cogn. Comput. Eng., vol. 5, no. March, pp. 161–169, 2024, doi: 10.1016/j.ijcce.2024.03.002.
  16. N. Lehmann et al., “Alkalinity responses to climate warming destabilise the Earth ’ s thermostat,” no. June 2022, 2023, doi: 10.1038/s41467-023-37165-w.
  17. G. Ke, Q. Meng, T. Finley, and T. Wang, “LightGBM : A Highly Efficient Gradient Boosting Decision Tree LightGBM : A Highly Efficient Gradient Boosting Decision Tree,” no. December, 2017.
  18. F. Alzamzami and M. Hoda, “Light Gradient Boosting Machine for General Sentiment Classification on Short Texts : A Comparative Evaluation,” vol. 8, 2020, doi: 10.1109/ACCESS.2020.2997330.
  19. M. Pritt and G. Chern, “Satellite Image Classification with Deep Learning,” IEEE Appl. Imag. Pattern Recognit. Work., pp. 1–7, 2017, doi: 10.1109/AIPR.2017.8457969.

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