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 waterspread
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.
