Abstract

This paper presents a supervised change detection technique for satellite images using a probabilistic neural network (PNN). The proposed method works in two phases. In the first phase a difference image is computed. The most commonly used techniques for computing the difference image such as ratio images or log ratio images degrade the performance of the algorithm in the presence of speckle noise. To overcome the above mentioned limitations the difference image in this work is computed using normalized neighborhood ratio based method. In the next phase the PNN is used to detect efficiently any change between the two images. An estimator is used by the PNN to estimate the probability density function. The ratio of two conditional probability density functions, called the likelihood ratio is computed. Finally, the log likelihood ratio test is used to classify the pixels of the difference image into changed and unchanged classes to create a change map. The change map highlights the changes that have occurred between the two input images. The proposed method was compared quantatively as well as qualitatively with other existing state of the art methods. The results showed that the proposed method outperforms the other methods.

Keywords

Change detection Probability density function Probabilistic neural network (PNN) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Green, K., Kempka, D., Lackey, L.: Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric Engineering and Remote Sensing 60, 331–337 (1994)Google Scholar
  2. 2.
    Lillesand, T.M., Kiefer, R.W.: Remote Sensing and Photo Interpretation, 3rd edn. John Wiley & Sons, New York (1994)Google Scholar
  3. 3.
    Bovolo, F., Marchesi, S., Bruzzone, L.: A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images. IEEE Transactions on Geoscience and Remote Sensing 50(6), 2196–2212 (2012)CrossRefGoogle Scholar
  4. 4.
    Bovolo, F., Bruzzone, L., Marconcini, M.: A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Trans. Geosci. Remote Sens. 46(7), 2070–2082 (2008)CrossRefGoogle Scholar
  5. 5.
    Celik, T.: Method for unsupervised change detection in satellite images. Electron. Lett. 46(9), 624–626 (2010)CrossRefGoogle Scholar
  6. 6.
    Yetgin, Z.: Unsupervised Change Detection of Satellite Images Using Local Gradual Descent. IEEE Transactions On Geoscience And Remote Sensing 50(5), 1919–1929 (2012)CrossRefGoogle Scholar
  7. 7.
    Bruzzone, L., Prieto, D.: Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 38(3), 1171–1182 (2000)CrossRefGoogle Scholar
  8. 8.
    Goh, A.T.: Probabilistic neural network for evaluating seismic liquefaction potential. Can. Geotech. J. 39(1), 219–232 (2002)CrossRefGoogle Scholar
  9. 9.
    Oliver, C., Quegan, S.: Understanding Synthetic Aperture Radar Images. Artech House, Norwood (1998)Google Scholar
  10. 10.
    Celik, T.: Change detection in satellite images using a genetic algorithm approach. IEEE Geosci. Remote Sens. Lett. 7(2), 386–390 (2010)CrossRefGoogle Scholar
  11. 11.
    Gong, M., Cao, Y., Wu, Q.: A neighborhood-based ratio approachfor change detection in SAR images. IEEE Geosci. Remote Sens. Lett. 9(2), 307–311 (2012)CrossRefGoogle Scholar
  12. 12.
    European Space agency, http://www.esa.int
  13. 13.
    Congalton, R.G.: A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37 (1), 35-46 (1991)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Akansha Mehrotra
    • 1
  • Krishna Kant Singh
    • 1
  • Kirat Pal
    • 1
  • M. J. Nigam
    • 1
  1. 1.Indian Institute of TechnologyRoorkeeIndia

Personalised recommendations