Research Papers

Super-resolution mapping of hyperspectral images for estimating the water-spread area of Peechi reservoir, southern India

[+] Author Affiliations
Shanmuga Priyaa Sakthivel

Anna University, Department of Geology, Chennai 600025, India

Heltin Genitha C.

St. Joseph’s College of Engineering, Department of Information Technology, Chennai 600119, India

Jeyakanthan V. Sivalingam

National Institute of Hydrology, Kakinada 533003, India

Sanjeevi Shanmugam

Anna University, Department of Geology, Chennai 600025, India

J. Appl. Remote Sens. 8(1), 083510 (Dec 05, 2014). doi:10.1117/1.JRS.8.083510
History: Received May 8, 2014; Accepted October 30, 2014
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Abstract.  Though the estimation of the water-spread area in reservoirs is often carried out by field surveys, it is time-consuming and tedious, and cannot be done periodically. To overcome this issue, satellite images are often used where the estimation is made through density slicing or conventional per-pixel classification. This results in an inaccurate estimation of reservoir capacity. The high cost and nonavailability of high-resolution images demands the use of an alternative approach that can give accurate information about the reservoir water-spread area. A hyperspectral image (Hyperion) of moderate resolution is used for the accurate estimation of the water-spread area of Peechi reservoir, southern India. The reservoir water-spread area obtained from per-pixel classification, subpixel classification, and super-resolution mapping approaches are compared with the water-spread area obtained from the ground truth hydrographic survey data. It is observed that the water-spread area estimated from the hyperspectral image by the per-pixel approach is 7.66sqkm, that by the subpixel approach is 6.34sqkm, and that by the super-resolution approach is 5.69sqkm compared to the actual area of 5.95sqkm. The classification accuracy estimated for the Hopfield neural network based super-resolution technique is 92.97%, whereas that for the conventional classifier (maximum likelihood) is 86.72%. This improved accuracy in classification resulted in an accurate estimation of water-spread area. Hence, it is inferred that super-resolution mapping applied to hyperspectral images is a computationally efficient approach for the accurate quantification of reservoir water-spread area.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Shanmuga Priyaa Sakthivel ; Heltin Genitha C. ; Jeyakanthan V. Sivalingam and Sanjeevi Shanmugam
"Super-resolution mapping of hyperspectral images for estimating the water-spread area of Peechi reservoir, southern India", J. Appl. Remote Sens. 8(1), 083510 (Dec 05, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083510


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