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.
Image classification has evolved from per-pixel to sub-pixel and from sub-pixel to super resolution mapping approaches. Super-resolution mapping (SRM) is a technique which allows mapping at the sub-pixel scale. Super-resolution mapping proves to be the better approach for the accurate classification of coarse spatial resolution images and to resolve mixed pixels in the boundary of such images. The accuracy of the super-resolved output depends on the input derived from the soft classification technique. This paper aims to compare the potential of support vector machine (SVM), spectral angle mapper (SAM) and linear spectral unmixing (LSU) as inputs for super-resolution mapping. The fraction image, distance measure image and probability image obtained from linear spectral unmixing, spectral angle mapper and support vector machine respectively are used as an input for super resolution mapping designed on Hopfield Neural Network (HNN) for the Hyperion image of Peechi reservoir, south India. Effectiveness of the inputs is evaluated by estimating the water-spread area of the Peechi reservoir from each of the outputs. The results indicate that the accuracy of any super-resolution approach depends on the inputs from the soft classification approaches. The accuracy of the water spread area estimated from the classified outputs is 95.9%, 96.6% and 99.7% from LSU, SAM and SVM respectively as inputs for the SRM method. Thus, the HNN based SRM method proves to be better when the soft classification input is from SVM.
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