REVOLUTIONIZING AIRCRAFT DETECTION IN HIGH RESOLUTION SATELLITE IMAGERY THROUGH DEEP LEARNING TECHNIQUES

Authors

  • Dr. A. Swetha, K. Mani teja, N. Varun, G. Rani, A. Rajesh Doi:10.48047/JOCAAA.34.4.552-561

Keywords:

Land Cover Change, Disaster Mitigation, Resource Management, Real-time Classification, Natural Disasters.

Abstract

India faces frequent natural disasters such as floods, droughts, cyclones, and landslides, which significantly impact lives, infrastructure, and the environment. Between 1990 and 2020, India reported over 400 natural disasters, affecting more than 1 billion people and causing damages worth billions.With 68% of its land prone to drought, 12% to floods, and 8% to cyclones, the need for effective disaster
mitigation strategies is paramount.

References

K. Islam, M. Jashimuddin, B. Nath, and T. K. Nath, “Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh,” The Egyptian Journal of Remote Sensing and Space Science, vol. 21, no. 1, pp. 37–47, Apr. 2018, doi:

1016/j.ejrs.2016.12.005.

C. Kok Yang, F. Pei Shan, and T. Lea Tien, “Climate change detection in Penang Island using deterministic interpolation methods,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 19, no. 1, pp. 412–419, Jul. 2020, doi: 10.11591/ijeecs.v19.i1.pp412-419.

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Published

2025-04-23

How to Cite

Dr. A. Swetha, K. Mani teja, N. Varun, G. Rani, A. Rajesh. (2025). REVOLUTIONIZING AIRCRAFT DETECTION IN HIGH RESOLUTION SATELLITE IMAGERY THROUGH DEEP LEARNING TECHNIQUES . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 552–561. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2336

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