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241 results on '"Sanjeevi, S"'

4. Rare Correlated High Utility Itemsets Mining: An Experimental Approach

Author

Lalitha Kumari, P., Sanjeevi, S. G., Madhusudhana Rao, T. V., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pattnaik, Prasant Kumar, editor, Rautaray, Siddharth Swarup, editor, Das, Himansu, editor, and Nayak, Janmenjoy, editor

Published
2018
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42. Estimation of Water-Spread Area of Singoor Reservoir, Southern India by Super Resolution Mapping of Multispectral Satellite Images.

Author

Shanmuga Priyaa, S., Jeyakanthan, V., Heltin Genitha, C., and Sanjeevi, S.

Abstract

Estimation of reservoir water-spread area is often carried out by field surveys which are cumbersome, time consuming, expensive and involves more man power. Hence, such surveys cannot be carried out periodically. To overcome this issue, satellite images are used, wherein the reservoir water-spread is estimated by conventional per-pixel classification such as the maximum likelihood and minimum distance to mean approaches that often results in inaccurate estimate of water-spread area due to the presence of mixed pixels. High cost and non-availability of high resolution images demands the use of an alternative approach that can give accurate information about the reservoir water-spread area. In this work, IRS, LISS III images of moderate (24 m) resolution were used for accurate estimation of the water-spread area of Singoor reservoir, southern India. The reservoir water-spread areas were extracted using per-pixel classification, sub-pixel classification and super resolution mapping approaches. These results were validated with the water-spread areas obtained from field data of the same dates. The error produced by the per-pixel approach was 6.66%, while it was 4.37% for the sub-pixel approach and a meagre 1.71% for the super-resolution approach. Fairly less error produced by the super resolution mapping technique implies that it is an efficient approach for accurate quantification of reservoir water-spread area. The estimated water-spread can be used in a simple volume estimation formula to estimate the capacity of the reservoir. [ABSTRACT FROM AUTHOR]

Published
2018
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