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

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.