A Hybrid Approach to Super-Resolution Mapping of Remotely Sensed Multi-spectral Satellite Images Using Genetic Algorithm and Hopfield Neural Network

  • C. Heltin GenithaEmail author
  • K. Vani
Research Article


Though super-resolution mapping of multi-spectral remote sensing satellite images is used to locate the multiple classes within a mixed pixel, it has limitations, for example, low rate of convergence leading to low accuracy and higher time consumption. This paper demonstrates a hybrid approach of genetic algorithm and Hopfield neural network which can not only speed up the process but also identify the exact global optimal solution using super-resolution mapping. The experiments are carried out for Landsat ETM + image of different dimensions (10 × 10, 25 × 25, 64 × 64, 128 × 128 and 256 × 256) and with map sizes of 2, 3, 4, 5 and 6. Map size indicates the number of all sub-pixels which are resolved from a pixel. The overall accuracy is 89.44%, 90.25%, 91.09%, 92.56%, and 93.62%, respectively, for map sizes 2, 3, 4, 5, and 6, thus showing an increase of nearly 2% accuracy for hybrid genetic algorithm. The time taken was also reduced by half of that for the genetic algorithm. Thus, the efficiency of this novel approach to map land cover classes in a coarse pixel with greater accuracy and appreciably lesser time has been demonstrated.


Hopfield Neural Network Super-resolution mapping Genetic algorithm Global optimal solution 


  1. Addink, E. A., De Jong, S. M., Davis, S. A., Dubyanskiy, V., Burdelov, L. A., & Leirs, H. (2010). The use of high-resolution remote sensing for plague surveillance in Kazakhstan. Remote Sensing of Environment, 114, 674–681.CrossRefGoogle Scholar
  2. Atkinson, P. M. (1997). Mapping sub-pixel boundaries from remotely sensed images. In Z. Kemp (Ed.), Innovations in GIS 4 (pp. 166–180). London: Taylor & Francis.Google Scholar
  3. Atkinson, P. M. (2005). Sub-pixel target mapping from soft-classified, remotely sensed imagery. Photogrammetric Engineering and Remote Sensing, 71, 839–846.CrossRefGoogle Scholar
  4. Boyd, D. S., & Foody, G. M. (2011). An overview of recent remote sensing and GIS based research in ecological informatics. Ecological Informatics, 6, 25–36.CrossRefGoogle Scholar
  5. Buermann, W., Saatchi, S., Smith, T. B., Zutta, B. R., Chaves, J. A., Mila, B., et al. (2008). Predicting species distributions across the Amazonian and Andean regions using remote sensing data. Journal of Biogeography, 35, 1160–1176.CrossRefGoogle Scholar
  6. Foody, G. M. (1992). On the compensation for chance agreement in image classification accuracy assessment. Photogrammetric Engineering and Remote Sensing, 58, 1459–1460.Google Scholar
  7. Foody, G. M. (2002). The role of soft classification techniques in the refinement of estimates of ground control point location. Photogrammetric Engineering and Remote Sensing, 68, 897–903.Google Scholar
  8. Heltin Genitha, C., (2014). Development and Implementation of algorithms for super resolution mapping of satellite images. Unpublished PhD thesis, Anna University, Chennai.Google Scholar
  9. Heltin Genitha, C., Vani, K. (2010). Super resolution mapping of satellite images using Hopfield Neural Networks. In Proceedings of IEEE international conference on recent advances in space technology services and climate change (RSTSCC) (pp. 114–118).Google Scholar
  10. Heltin Genitha, C., & Vani, K. (2013). Sub-pixel classification using FCM and FWCM algorithms. In Fiftieth International conference on advanced computing.Google Scholar
  11. Mertens, K. C., Verbeke, L. P. C., Ducheyne, E. I., & Wulf, R. R. D. (2003). Using genetic algorithms in sub-pixel mapping. International Journal of Remote Sensing, 24, 4241–4247.CrossRefGoogle Scholar
  12. Nguyen, M. Q., Atkinson, P. M., & Lewis, H. G. (2005). Super-resolution mapping using a Hopfield Neural Network with LIDAR data. IEEE Geoscience and Remote Sensing Letters, 2, 366–370.CrossRefGoogle Scholar
  13. Nguyen, M. Q., Atkinson, P. M., & Lewis, H. G. (2006). Super-resolution mapping using a Hopfield Neural Network with fused images. IEEE Transactions on Geoscience and Remote Sensing, 44, 736–749.CrossRefGoogle Scholar
  14. Tatem, A. J., Lewis, H. G., Atkinson, P. M., & Nixon, M. S. (2001). Super-resolution target identification from remotely sensed images using a Hopfield Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 39, 781–796.CrossRefGoogle Scholar
  15. Thornton, M. W., Atkinson, P. M., & Holland, D. A. (2006). Super resolution mapping of rural land cover features from fine spatial resolution satellite sensor imagery. International Journal of Remote Sensing, 27, 473–491.CrossRefGoogle Scholar
  16. Verhoeye, J., & Wulf, R. D. (2002). Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sensing of Environment, 79, 96–104.CrossRefGoogle Scholar
  17. Wang, Q., Wang, L., & Liu, D. (2012). Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery. International Journal of Remote Sensing, 33, 6480–6496.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Department of Information TechnologySt. Joseph’s College of EngineeringChennaiIndia
  2. 2.Department of Information Science and TechnologyAnna University, ChennaiChennaiIndia

Personalised recommendations