Content Based Satellite Cloud Image Retrieval and Rainfall Estimation using Shape Features

International Journal of Geoinformatics and Geological Science

© 2017 by SSRG - IJGGS Journal

Volume 4 Issue 1

Year of Publication : 2017

Authors : D.Chandraprakash and M. Narayana

10.14445/23939206/IJGGS-V4I2P105

How to Cite?

D.Chandraprakash and M. Narayana, "Content Based Satellite Cloud Image Retrieval and Rainfall Estimation using Shape Features," SSRG International Journal of Geoinformatics and Geological Science, vol. 4,  no. 1, pp. 34-39, 2017. Crossref, https://doi.org/10.14445/23939206/IJGGS-V4I2P105

Abstract:

In the last decade we witnessed a large increase in data generated by earth observing satellites. But today Satellite Image Retrieval is a big issue to discuss. There is huge amount of research work focusing on the retrieving of images in the image database. One of the most important steps in earlier stages of satellite image processing is cloud detection. Therefore, the satellite cloud images provide a valuable source of information in weather forecasting and early prediction of different atmospheric disturbances such as typhoons, hurricanes and also in the estimation of rainfall. Rainfall is the primary source for water. Rainfall forecasting is important for agriculture and living things. The type of rainfall is predicted by analyzing the size and shape of the cloud images. Shape is important feature in the meteorological satellite images. Different types of clouds have different shapes. The content based image retrieval with the fast and high matching retrieving ability is the need of the day for shape mining. The ultimate focus of this project reports to develop a Content Based Image Processing and Information as well as image retrieval (CBIPR) system using shape feature , for the retrieval of the satellite cloud images and also to forecast the rainfall from the meteorological satellite archival that allows us to study the past weather system and understand the current weather system

Keywords:

semantic gap, typhoons, hurricanes, aquatic, retrieval.

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