Advertisement

A Hybrid Approach to Super Resolution Mapping for Water-Spread Area and Capacity Estimation of Reservoir Using Satellite Image (India)

  • Heltin Genitha Cyril Amala Dhason
  • Indhumathi Muthaia
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

For proper monitoring and scheduling of the supply of drinking water in reservoirs, it is necessary to carry out the capacity surveys. Remote sensing techniques can be used to estimate the capacity of the reservoirs in an inexpensive and less laborious way. In this paper, a super resolution mapping based on hybrid approach was developed and applied to Landsat OLI image of the Puzhal reservoir, Chennai city, southern India and the reservoir water-spread area was estimated. The estimated water-spread was used to find the capacity of the reservoir using Trapezoidal formula. The hybrid approach uses New Fuzzy Cluster Centroid (NFCC) algorithm for sub-pixel mapping and multi-objective genetic algorithm for super resolution mapping. The super resolution mapping is an advanced classification technique which accurately maps the location of classes within a pixel. The capacity determined from the image processing technique is compared with that estimated from the field survey data with a meagre 1.35% error. Hence, it is observed that the super resolution mapping is a prominent methodology to estimate the water-spread area of the reservoir which in turn increases the accuracy of the estimation capacity of the reservoir.

Keywords

Sub-pixel mapping Super resolution mapping Satellite image Water-spread area 

References

  1. 1.
    Nguyen, Q., Atkinson, P.M., Lewis, H.G.: Super-resolution mapping using Hopfield neural network with panchromatic imagery. Int. J. Remote Sens. 32(21), 6149–6176 (2011)CrossRefGoogle Scholar
  2. 2.
    Thornton, M.W., Atkinson, P.M., Holland, D.A.: Super resolution mapping of rural land cover features from fine spatial resolution satellite sensor imagery. Int. J. Remote Sens. 27, 473–491 (2006)CrossRefGoogle Scholar
  3. 3.
    Verhoeye, J., Wulf, R.D.: Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sens. Environ. 79, 96–104 (2002)CrossRefGoogle Scholar
  4. 4.
    Wang, Q., Wang, L., Liu, D.: Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery. Int. J. Remote Sens. 33, 6480–6496 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Heltin Genitha Cyril Amala Dhason
    • 1
  • Indhumathi Muthaia
    • 2
  1. 1.Department of Information TechnologySt. Joseph’s College of EngineeringChennaiIndia
  2. 2.Department of Civil EngineeringSt. Joseph’s College of EngineeringChennaiIndia

Personalised recommendations