Dr. Heltin Genitha C. Profile

Dr. Heltin Genitha C.

at St Josephs College of Engineering

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SPIE Journal Paper | 5 December 2014

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

Shanmuga Priyaa Sakthivel, Heltin Genitha C., Jeyakanthan Sivalingam, Sanjeevi Shanmugam

JARS, Vol. 8, Issue 01, 083510, (December 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083510

KEYWORDS: Super resolution, Neurons, Hyperspectral imaging, Image classification, Vegetation, Image analysis, Associative arrays, Error analysis, Image resolution, Neural networks

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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 7.66 sq km, that by the subpixel approach is 6.34 sq km, and that by the super-resolution approach is 5.69 sq km compared to the actual area of 5.95 sq km. 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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