Rohit V. Ingole

Work place: Department of Data Science, G H Raisoni College of Engineering, Nagpur, Maharashtra, India

E-mail: rohit.ingole.ds@ghrce.raisoni.net

Website: https://orcid.org/0009-0009-1951-4726

Research Interests: Natural Language Processing, Image Processing, Data Analysis, Data Processing, Data Science, Deep Learning

Biography

Rohit V. Ingole is currently pursuing a B. Tech in Data Science at G H Raisoni College of Engineering, Nagpur, Maharashtra. He is also a student member of IEEE. He has published 3 research papers with IEEE which can be accessed at IEEE Xplore Digital Library, Scopus and Google Scholar. Rohit is the Vice-Chair at IEEE CIS SBC GHRCE chapter conducting and managing numerous events and bootcamps. His research interests include Machine Learning, Deep Learning, Image Processing, Data Science, Data Analysis and Natural Language Processing.

Author Articles
Refining Cyclonic Cloud Analysis via INSAT-3D Satellite Imagery and Advanced Image Processing Techniques

By Viraj R. Thakurwar Rohit V. Ingole Aditya A. Deshmukh Rahul Agrawal Chetan Dhule Nekita Chavhan Morris

DOI: https://doi.org/10.5815/ijigsp.2024.05.06, Pub. Date: 8 Oct. 2024

Cyclones, with their high-speed winds and enormous quantities of rainfall, represent severe threats to global coastal regions. The ability to quickly and accurately identify cyclonic cloud formations is critical for the effective deployment of disaster preparedness measures. Our study focuses on a unique technique for precise delineation of cyclonic cloud regions in satellite imagery, concentrating on images from the Indian weather satellite INSAT-3D. This novel approach manages to achieve considerable improvements in cyclone monitoring by leveraging the image capture capabilities of INSAT-3D. It introduces a refined image processing continuum that extracts cloud attributes from infrared imaging in a comprehensive manner. This includes transformations and normalization techniques, further augmenting the pursuit of accuracy. A key feature of the study's methodology is the use of an adaptive threshold to correct complications related to luminosity and contrast; this enhances the detection accuracy of the cyclonic cloud formations substantially. The study further improves the preciseness of cloud detection by employing a modified contour detection algorithm that operates based on predefined criteria. The methodology has been designed to be both flexible and adaptable, making it highly effective while dealing with a wide array of environmental conditions. The utilization of INSAT-3D satellite images maximizes the performing capability of the technique in various situational contexts. 

[...] Read more.
Other Articles