Multi-Temporal Satellite Image Analysis Using Gene Expression Programming

  • J. Senthilnath
  • S. N. Omkar
  • V. Mani
  • Ashoka Vanjare
  • P. G. Diwakar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)

Abstract

This paper discusses an approach for river mapping and flood evaluation to aid multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation to extract water covered region. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images is applied in two stages: before flood and during flood. For these images the extraction of water region utilizes spectral information for image classification and spatial information for image segmentation. Multi-temporal MODIS images from “normal” (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as artificial neural networks and gene expression programming to separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water region. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification and region-based segmentation is an accurate and reliable for the extraction of water-covered region.

Keywords

MODIS satellite image Gene expression programming Artificial neural network 

Notes

Acknowledgments

This work is supported by the Space Technology Cell, Indian Institute of Science, Bangalore and Indian Space Research Organization (ISRO). We also acknowledge the MODIS mission scientists and associated NASA personnel for the production of the remote sensing data which is used in this paper.

References

  1. 1.
    Brakenridge, R., Anderson, E.: MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications. Transboundary Floods: Reducing Risks through Flood Management. Springer-Verlag, pp. 1–12 (2006)Google Scholar
  2. 2.
    Khan, S.I., Hong, Y., Wang, J., Yilmaz, K.K., Gourley, J.J., Adler, R.F., Brakenridge, G.R., Policelli, F., Habib, S., Irwin, D.: Satellite remote sensing and hydrologic modeling for flood inundation mapping in Lake Victoria Basin: implications for hydrologic prediction in ungauged basins. IEEE Trans. Geosci. Remote Sens. 49, 85–95 (2011)CrossRefGoogle Scholar
  3. 3.
    Mingjun, S., Daniel, C.: Road extraction using SVM and image segmentation. Photogram. Eng. Remote Sens. 70(12), 1365–1371 (2004)Google Scholar
  4. 4.
    Senthilnath, J., Rajeswari, M., Omkar, S.N.: Automatic road extraction using high resolution satellite image based on texture progressive analysis and normalized cut method. J Indian Soc. Remote Sens. 37(3), 351–361 (2009)CrossRefGoogle Scholar
  5. 5.
    Omkar, S.N., Senthilnath, J., Mudigere, D., Manoj Kumar, M.: Crop classification using biologically inspired techniques with high resolution satellite image. J. Indian Soc. Remote Sens. 36(2), 172–182 (2008)Google Scholar
  6. 6.
    Senthilnath, J., Omkar, S.N., Mani, V., Tejovanth, N., Diwakar, P.G., Archana Shenoy, B.: Hierarchical clustering algorithm for land cover mapping using satellite images. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 5(3), 762–768, (2012)Google Scholar
  7. 7.
    Wang, Z., Zhang, D.: Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans. Circuits Syst. II(46), 78–80 (1999)Google Scholar
  8. 8.
    Haykin, S.: Neural Networks—A Comprehensive Foundation, 2nd edn. Pearson Prentice Hall Publication, New Jersey (1994)Google Scholar
  9. 9.
    Omkar, S.N., Sivaranjani, V., Senthilnath, J., Mukherjee, S.: Dimensionality reduction and classification of hyperspectral data. Int. J. Aerosp. Innov. 2(3), 157–163 (2010)Google Scholar
  10. 10.
    Omkar, S.N., Senthilnath, J.: Integration of Swarm Intelligence and Artificial Neutral Network, Neural Network and Swarm Intelligence for Data Mining, Chapter 2. In: Dehuri, S., et al. World Scientific Press, Singapore, pp. 23–65 (2011)Google Scholar
  11. 11.
    Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst. 13(2), 87–129 (2001)MATHGoogle Scholar
  12. 12.
    Senthilnath, J., Shivesh, B., Omkar, S.N., Diwakar, P.G., Mani, V.: An approach to multi-temporal MODIS image analysis using image classification and segmentation. Adv. Space Res. 50(9), 1274–1287 (2012)CrossRefGoogle Scholar
  13. 13.
    Arvind, C.S.: Ashoka Vanjare, Omkar, S.N., Senthilnath, J., Mani, V., Diwakar, P.G.: Multi-temporal satellite image analysis using unsupervised techniques. Adv. Comput. Inf. Technol. Adv. Intell. Syst. Comput. 177, 757–765 (2012)Google Scholar
  14. 14.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • J. Senthilnath
    • 1
  • S. N. Omkar
    • 1
  • V. Mani
    • 1
  • Ashoka Vanjare
    • 1
  • P. G. Diwakar
    • 2
  1. 1.Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia
  2. 2.Earth Observation SystemISRO Head QuartersBangaloreIndia

Personalised recommendations