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Remote Sensing Data Analysis in Machine Learning and Proposed Quantum Computational Intelligence: A Meta-Analysis

Deep learning and machine learning are the top ranking techniques applied in objects classification in remote sensing data. We have conducted a meta-analysis and find out that feature selection is an important achievement in Machine Learning algorithms however, the following challenges were identified; Machine learning need large datasets for training and satellite images contain a lot of noise which may be classify as an object so it is not suitable for object detection in satellite images, Detection accuracy in machine learning depend on the quality of training datasets and finally Biased feature selection may led to the incorrect classification of objects in satellite images. While Most of the deep learning techniques suffer from data preprocessing problems especially when applying in satellite images because satellite images contain a lot of noise. Therefore the requirement of quality and quantity of training datasets is very high. The designed, development, improvement and adjustment of deep learning techniques to suit a specific research is still rely on the experience of the developer which is also a challenging issue. Application of deep learning techniques in remote sense data are still in an infant state because based on our review only few numbers of articles are published from Africa countries. We have suggested that quantum computational intelligence to be applied in remote sensing data analysis.

Deep Learning, Machine Learning, Satellite Image, Quantum Computational Intelligence, Remote Sensing

Ibrahim Goni, Asabe Sandra Ahmadu, Yusuf Musa Malgwi. (2022). Remote Sensing Data Analysis in Machine Learning and Proposed Quantum Computational Intelligence: A Meta-Analysis. Communications, 9(2), 6-10.

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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Dey, N., H., A. E., Bhatt, C., Ashour, A. S. & Satapathy, S. C. (2018). Internet of Things and Big Data Analytics Toward Next-Generation Intelligence. Springer International Publishing AG.
2. Wenxue, F., Jianwen, M., Pei, C., & Fang C. (2020). Remote sensing satellites for digital earth. 4th edition, Manual of digital earth Ltd. Beijin China.
3. Lingli, Z., Juha, S., Jingbin, L., Juha, H., Harri, K. & Henrik, H., (2018). Multi-purposeful application of geospartial data. IntechOpen Ltd Beijing, China.
4. Jude, D. H. & Vania, V. E., (2017). Deep learning for image processing applications IOS Press Asterdam, Berlin and Washington DC.
5. Bai, X., Zhang, H. & Zhou, J., (2014). VHR object detection based on structural feature extraction and query expansion. IEEE Transaction of Geosciences Remote sensing 52, 6508-6520.
6. Malek, S., Benz, Y., Alajlan, N., Alhichri, H. & Melgani, F. (2014). Efficient framework for palm tree detection in UAV images. IEEE Journal Selection Topics of Application in Earth Observation Using Remote Sensing. 7, 4692-4703.
7. Alshehhi, R., Marpu, P. R., Woon, W. L., & Dalla, M. (2017). Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing. 130, 139-149.
8. Ajeet, R. P., Manjusha, P., & Siddharth, R. (2018). Application of deep learning for object Detection. International Conference on Computational Intelligence and Data Science (ICCIDS 2018).
9. Absalberg, A. (2015). Detection of seals in remote sensing images using features extracted from deep convolutional neutral networks. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1893-1986.
10. Abhishek, G., Alagan A., Ling, G. & Ahmed, S. K. (2021). Deep learning for object detection and Scene perception in self-driving cars: Survey, challenges, and open issues. Science Direct Array 10 (2) 157-171.
11. Arshitha, F. & Biju, K. S. (2020). Accurate detection of building from satellite images using CNN. In Proceeding of the 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE) 12-13 June 2020, Istanbul, Turkey.
12. Ahmad, M. Wessam, M. H. & Ehab, S. (2019). Small Objects Detection in Satellite Images Using Deep Learning. 2019 IEEE 9th International Conference on Intelligent Computing and Information Systems (ICICIS). 194-207 Cairo Egypt.
13. Alexander, A. S. G., Ilma, A. & Edy, I. (2020). Semantic segmentation of Aerial imagery for road Extraction with deep learning, ICIC Express letter, 14 (1), 43-51.
14. Chen, Y., Lin, Z., Zhao, X., Wang, G. & Gu, Y. (2017). Deep learning-based classification of hyper spectral data. IEEE J. sel. Top. Appl. Earth Obs. Remote Sensing. 7 (6), 2094-2107.
15. Boualleg, Y. & Farah, M. (2018). Enhanced interactive remote sensing image retrieval with scene Classification convolutional neural networks model. In Proceedings of the IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018. 4748–4751.
16. Chen, Y., Zhao, X. & Jia, X. (2015). Spectral-spatial classification of hyper spectral data based on deep belief network. IEEE J. sel. Top. Appl. Earth Obs. Remote sens. 8 (6), 2381-2392.
17. Cheng, G., & Han, J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing. 117, 11-28.
18. Claudia, P. D. & Shashikala, K. P. (2020). Satellite image processing to detect building using deep learning. International Journal of Innovative Research in Electrical Electronics, Instrumentation and control engineering, 8 (9), 17-25.
19. Castelluccio, M., Poggi, G., Sansone, & Verdoliva, C. (2015). Land use Classification in Remote Sensing Images by Convolutional Neural Networks. arXio 2015; 1-11, rXiv: 1508.00092.
20. Duan, F., Liu, L., Jiao, P., Zhao, L. & Zhang, L. (2017). SAR image segmentation based on convolutional-wavelet neural network and Markov random field, Pattern Recognition, 64, 255-267.
21. Deepthi, S., Sandeep, K. & Suresh, L. (2021). Detection and Classification of Objects in Satellite Images using Custom CNN. International Journal of Engineering Research & Technology. 10 (6), 629-635.
22. Deng, Z., Sun, H., Zhao, S., Lei, L. & Zou, H. (2011). Multi-scale object detection in remote sensing imagery with convolution neural networks. ISPRS Journal Photogrammetry and Remote Sensing. 145, 3-22.
23. Duarte, D. (2018). Satellite image classification of building damages using air bone and satellite image sampling in deep learning approach. ISPR Annals of Photogrammetry Remote Sensing and Spartial information science. 4 (2), 1-17.
24. Erhan, D., Christian, S., Alexander, T. & Dragomir, A. (2014). Scalable object detection using deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2147–54.
25. Eisnak, C., Dragut, L., & Blascke, T., (2011). A generic procedure for semantics Oriented landform classification using object-based image analysis. Geo-morphometry. 125-128.
26. Gao, L., Song, W., Dai, J., & Chen, Y. (2019). Road extraction from high resolution remote sensing imagery using refined deep residual convolutional neural network, Remote Sensing. 11 (5). 552.
27. Gao, F., Huang, T., Wang, J., Sun, J., Hussain, A. & Yang, E. (2017). Dual-branch deep convolution neural network for Polari metric SAR image classification. Applied Science. 7, 447.
28. Ghamisi, P., Chen, Y., & Zhu, X. (2016). A Self-improving convolution Neural network for the classification of hyper spectral data, IEEE Journal selection Geoscience and Remote Sensing Letter, 13 (10) 1537-1541.
29. Geng, J., Fan, H., Wang, X., Ma, B., Li, B. & Chen, F. (2015). High-resolution SAR image classification via deep convolutional auto-encoders IEEE Geoscience and remote sensing letter 12, (11), 2351-2355.
30. Geng, J., Wang, H., Fan, J. & Ma, X. (2017). Deep supervised and contractive neural network for SAR image classification. IEEE Transaction Geoscience Remote Sensing 55 (4), 2442-2459, 2017.
31. Guoji W., Wu, M., Wei, X. & Song, H. (2020). Water identification from high resolution remote sensing image based on multidimensional densely connected convolutional neural networks, Remote Sensing, 12 (5), 795.
32. Gao, L., Song, W., Dai, J., & Chen, Y., (2019). Road extraction from high-resolution remote sensing imagery using refined Deep Residual Convolutional neural network Remote sensing. 11, 553.
33. Zhang, C., Sargent, Pan, X., Li, H., Gardiner, A., Hare, J. & Atitinson, P. M. (2018). An object-based convolutional neutral networks (OCNN) for urban land use classification. Remote Sensing Environment. 216, 57-70.
34. Zou, Q., Ni, L., Zhang, T. & Wang, Q., (2015). Deep Learning based feature selection for remote Sensing scene classification. IEEE Geoscience Remote Sensing Letter 12 (11), 2321-2325.