Remote detection and sensing of ground and environmental targets using artificial orifice radar (SAR) and deep learning
Subject Areas : New technologies in natural resources and environment
Gohar Varamini
1
,
Amin Eskandari
2
,
Amin Tousi
3
1 - Department of Electrical Engineering, Bey. C., Islamic Azad University, Beyza, Iran.
2 - دانشگاه آزاد اسلامی، واحد شیراز، گروه مهندسی برق و کامپیوتر، شیراز، ایران
3 - 1Department of Electrical Engineering, Shi. C., Islamic Azad University, Beyza, Iran.
Keywords: Artificial Window Radar (SAR), Deep Learning, Artificial Intelligence, Remote Sensing,
Abstract :
Introduction: The technology of detection, identification and detection of objects and the environment is one of the most important parameters in engineering and environmental sciences. It is also widely used in the field of deep learning and artificial intelligence domains and plays a significant role in recognizing, identifying, and reconstructing images. This technology is a prelude to many projects and algorithms in this field, in which objects are identified in satellite and radar images and separated from surplus information and data, and in the next stages, they are classified and finally measured.
Materials and Methods: The method of identifying ground targets by SAR radar is using deep learning and artificial neural networks. In the pre-processing stage, in order to reduce the destructive effects of noise and preserve the original data, the Lee filter has been used due to its better efficiency in improving image quality and better preservation of edges, and the separation of excess data from the original images has been used using the blind signal separation algorithm.
Results and Discussion: In this study, the environmental conditions of the geographical area have been investigated through remote sensing and the proposed method in order to classify the data from the combined deep learning network, the results of which have been compared with the methods proposed in the field of goal classification, and finally it is determined that the proposed network in terms of classification accuracy with 86.31% compared to other studied networks with accuracy and coefficient. Higher reliability can be checked and implemented. Finally, using YOLO and RCNN algorithms to investigate the results of the detection and identification of ground targets
It has been discussed that the RCNN algorithm has been denoised with higher accuracy in detecting and identifying images.
Conclusion: Finally, the performance and reliability of the proposed model can be implemented with more accuracy and very little error. It will create and provide a very desirable performance with a high reliability factor in reducing the destructive effects and better efficiency of the system
1. H. R. E.-H. M. S. Y. AbubakarShaabana;, "Decomposition based multiobjective evolutionary algorithm for PV/Wind/Diesel Hybrid Microgrid System design considering load uncertainty," Energy Reports, vol. 7,
pp. 52-69, 2021. 2. N. Eghtedarpour and E. Farjah, "Power control and management in a hybrid AC/DC microgrid," Smart
Grid, IEEE Transactions on, vol. 5, pp. 1494-1505, 2014. 3. Y. B. A Rafiee, H Bevrani, T Kato, "Robust MIMO Controller Design for VSC-based Microgrids: Sequential Loop Closing Concept and Quantitative Feedback Theory," IEEE Transactions on Smart Grid,
2021. 4. X. Zhang, C. Huang, and J. Shen, "Energy Optimal Management of Microgrid with High Photovoltaic
Penetration," IEEE Transactions on Industry Applications, 2022. 5. L. Zhang, Y. Yang, Q. Li, W. Gao, F. Qian, and L. Song, "Economic optimization of microgrids based on peak shaving and CO2 reduction effect: A case study in Japan," Journal of Cleaner Production, vol. 321, p.
128973, 2021. 6. A. H. V.-H. B. H.-M. Kim, "Resilience-Oriented Optimal Operation of Networked Hybrid Microgrids,"
IEEE Transactions on Smart Grid, vol. 10, 2019 7. K. R. C. C. C. R. Zhang, "Energy Cooperation Optimization in Microgrids With Renewable Energy
Integration," IEEE Transactions on Smart Grid, vol. 9, 2018. 8. C. J. Dongfeng Yang, Guowei Cai, Deyou Yang, Xiaojun Liu,, "Interval method based optimal planning of multi-energy microgrid with uncertain renewable generation and demand," Applied Energy, vol. 277,
2020. 9. Z. X. Xiaohong Guan, and Qing-Shan Jia, "Energy-Efficient Buildings Facilitated by Microgrid," IEEE
Transaction on Smart Grid, vol. 1, 2010. 10. S. M. M. Liaqat Ali, Hamed Bizhani, Arindam Ghosh,, "Optimal planning of clustered microgrid using
a technique of cooperative game theory,," Electric Power Systems Research, vol. 183, 2020. 11. P. L. Mansour Alramlawi, "Design Optimization of a Residential PV-Battery Microgrid With a Detailed Battery Lifetime Estimation Model," IEEE Transactions on Industry Applications, vol. 46, 2020. Industry Applications, vol. 46, 2020. Battery Lifetime Estimation Model," IEEE Transactions on Industry Applications,
vol. 46, 2020. 12. Atteia G, Collins MJ, Algarni AD, Samee NA. Deep-learning-based feature extraction approach for significant wave height prediction in SAR mode altimeter data. Remote Sens. 2022; 14(21):5569.
https://doi.org/10.3390/rs14215569 13. Behar Kamran Z, Amir Etemad S, Mohsen H. Determination of effective parameters in forecasting
significant wave height in Neka. In: 4th International Conference on Offshore Industries; 2011. [In Persian] 14.
Breiman L. Bagging predictors. Mach Learn. 1996;24(2):123-40. https://doi.org/10.1007/BF00058655 15.
bualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH. The arithmetic optimization algorithm.
Comput Methods Appl Mech Eng. 2021; 376:113609. https://doi.org/10.1016/j.cma.2020.113609 16.
Cox DR, van der Meer JW. Wave forces on marine structures. CRC Press; 2010. 17. Davis RE, Collins DC, Wu L. Effects of temperature and salinity on wave properties and their impact
on coastal infrastructure. J Coast Res. 2014;30(3):578-88. https://doi.org/10.2112/JCOASTRES-D-13-00048.1 18. Dhodhi J, Sharma K, Hassanin A. Safety of offshore platforms. In: Encyclopedia of Maritime and
Offshore Engineering. Wiley; 2017. p. 1-10. https://doi.org/10.1002/9781118476406.emoe0021 19. Feng Z, Hu P, Li S, Mo D. Prediction of significant wave height in offshore China based on the
machine learning method. J Mar Sci Eng. 2022;10(6):836. https://doi.org/10.3390/jmse10060836 20. Han L, Ji Q, Jia X, Liu Y, Han G, Lin X. Significant wave height prediction in the South China Sea based
on the ConvLSTM algorithm. J Mar Sci Eng. 2022;10(11):1683. https://doi.org/10.3390/jmse10111683 21. Harrison S, MacDonald A. Influence of seabed sediment composition on wave propagation and
sediment transport. Coast Eng. 2015; 102:1-12. https://doi.org/10.1016/j.coastaleng.2015.04.001 22. Li Z, Zhang M, Wang X. Deep learning for predicting wave height and its implications for coastal
engineering. Eng Appl Artif Intell. 2021;98:104064. https://doi.org/10.1016/j.engappai.2021.104064 23. Lotfollahi Yeganeh M, Lashtah Neshayi M, Ghorbani M, Larian MB. Modeling and forecasting of significant wave height in the Caspian Sea using chaos theory. Amirkabir J Civ Eng. 2013; 45:97-105. [In
Persian] 24. Mishra JP, Singh K, Chaudhary H. Intelligent Ocean wave height prediction system using light GBM model. Int J Syst Innov. 2022;7(2):64-77
25. A. Ajami and M. Daneshvar, "Data driven approach for fault detection and diagnosis of turbine in thermal power plant using Independent Component Analysis (ICA)" International journal of Electrical Power and Energy Systems , Vol.43, pp. 728–735, 2012.
