Cyclone and Earthquake Recognition and Estimation using HSV Colour Segmentation and Clustering

Rosy Mishra¹ , Rajaram Meher² , Pratibha Bhoi³ , Amisha Mohanty⁴

Section:Research Paper, Product Type: Journal
Vol.7 , Issue.4 , pp.8-20, Aug-2019

Online published on Aug 31, 2019

Copyright © Rosy Mishra, Rajaram Meher, Pratibha Bhoi, Amisha Mohanty . This 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.
 

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Abstract :
Natural calamities are a threat to human civilization since ancient age. Advancement of technology leads to attract many researchers to pre-prediction of natural disaster such as Earthquake and Cyclone. Even it is a great challenge to estimate the post damage. This paper depicts the Cyclone prior prediction using novel HSV based color segmentation using unsupervised classification approach using K-means clustering. For the better survival of people and also economic point of view, it is most important to know the causes and consequences of natural calamities like earthquake and cyclone. This can be achieved through this research work by determining various combination of k-means clustering algorithm and using correspondence filters. The research not only provides information about applications of image processing techniques but it also provides a quick, easy, effortless knowledge about the effect of cyclone and earthquake in a given area. Rapid advances in k-means clustering have made it possible to obtain images of the atmosphere using different HSV technologies, make weather prediction better.

Key-Words / Index Term :
HSV (Hue Saturation Value), Histogram, K-means clustering

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