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Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

Romany F. Mansour1,*, Eatedal Alabdulkreem2

1 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt
2 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

* Corresponding Author: Romany F. Mansour. Email: email

Computer Systems Science and Engineering 2023, 44(2), 1161-1169.


The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image Classification Algorithm (PICA) system is introduced to implement an image processing technique, detect disasters, and determine results with the help of the PICA, which allows disaster analysis to be extracted more effectively. The PICA is essential to overcoming strong shadows, for proper access to disaster characteristics to false positives by operators, and to false predictions that affect the impact of the disaster. The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches. Two types of proposed PICA systems detect disasters faster and more accurately (95.6%).


Cite This Article

R. F. Mansour and E. Alabdulkreem, "Disaster monitoring of satellite image processing using progressive image classification," Computer Systems Science and Engineering, vol. 44, no.2, pp. 1161–1169, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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