Advertisement

Classification of High Resolution Satellite Images Using Equivariant Robust Independent Component Analysis

  • Pankaj Pratap Singh
  • R. D. Garg
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

Classification approach helps to extract the important information from satellite images, but it is quite effective on extracting the information from mixed classes. The problem of classes is well known in the area of satellite image processing due to similar spectral resolution among the few classes (objects). It is quite obvious in multispectral data which is having little variation in spectral resolution with heterogeneous classes. In earlier times, Neural network based classification has been used widely, but at a cost of high time and computation complexity. To resolve the problem of the mixed classes due to the spectral behavior in sufficient time, a novel method Equivariant Robust Independent Component Analysis (ERICA) is proposed. This algorithm separates the objects from mixed classes, which shows similar spectral behavior. It can easily predict the objects without using of pre-whitening technique. Therefore, pre-whitening is not playing an important role in convergence of the algorithm. Due to Quasi-Newton based iteration in this algorithm helps to converge to a saddle point with locally isotropic convergence, regardless of the spatial and spectral distributions of satellite images. Hence, this proposed ERICA gives major contribution for classification of satellite images in healthy trees, buildings and road areas. Another important one in the image is shadow information, which helps to show elevated factor due to high rising buildings and flyovers in emerging suburban areas. The experimental results of the remote sensing data clearly indicates that the proposed ERICA has better classification accuracy and convergence speed, and is also appropriate to solve the image classification problems.

Keywords

quivariant Robust Independent Component Analysis Information extraction Image classification Mixed class Performance index 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Almeida, L.B., Silva, F.M.: Adaptive Decorrelation. In: Aleksander, I., Taylor, J. (eds.) Artificial Neural Networks, vol. 2, pp. 149–156. Elsevier Science Publishers (1992)Google Scholar
  2. 2.
    Amari, S.: Natural gradient work efficiently in learning. Neural Comput. 10(2), 251–276 (1998)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Benediktsson, J.A., Pesaresi, M., Arnason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing 41(9), 1940–1949 (2003)CrossRefGoogle Scholar
  4. 4.
    Cardoso, J.F., Laheld, B.: Equivariant adaptive source separation. IEEE Trans. Signal Process. 44(12), 3017–3030 (1996)CrossRefGoogle Scholar
  5. 5.
    Choi, S., Cichocki, A., Amari, S.: Flexible independent component analysis. Journal of VLSI Signal Processing 26(1/2), 25–38 (2000)CrossRefMATHGoogle Scholar
  6. 6.
    Cichocki, A., Unbehauen, R., Rummert, E.: Robust learning algorithm for blind separation of signals. Electronics Letters 30(17), 1386–1387 (1994)CrossRefGoogle Scholar
  7. 7.
    Cichocki, A., Georgiev, P.: Blind Source Separation Algorithms with Matrix Constraints. IEICE Trans. on information and Systems, Special Section on Special issue on Blind Signal Processing  E86-A(1), 522–531 (2003)Google Scholar
  8. 8.
    Cruces, S., Castedo, L., Cichocki, A.: Novel Blind Source Separation Algorithms Using Cumulants. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, V, Istanbul, Turkey, pp. 3152–3155 (2000)Google Scholar
  9. 9.
    Kelley, C.T.: Iterative methods for linear and nonlinear equations. In: Frontiers in Applied Mathematics, vol. 16, pp. 71–78. SIAM, Philadelphia (1995)Google Scholar
  10. 10.
    Li, G., Wan, Y., Chen, C.: Automatic building extraction based on region growing, mutual information match and snake model. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds.) ICICA 2010. CCIS, vol. 106, pp. 476–483. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Segl, K., Kaufmann, H.: Detection of small objects from high-resolution panchromatic satellite imagery based on supervised image segmentation. IEEE Transactions on Geoscience and Remote Sensing 39(9), 2080–2083 (2001)CrossRefGoogle Scholar
  12. 12.
    Singh, P.P., Garg, R.D.: A Hybrid approach for Information Extraction from High Resolution Satellite Imagery. International Journal of Image and Graphics 13(2), 340007(1-16) (2013)Google Scholar
  13. 13.
    Yang, J.H., Liu, J., Zhong, J.C.: Anisotropic diffusion with morphological reconstruction and automatic seeded region growing for colour image segmentation. In: Yu, F. (ed.) Proceedings of the International Symposium on Information Science and Engineering, Shangai, China, pp. 591–595 (2008)Google Scholar
  14. 14.
    Zhang, L., Amari, S., Cichocki, A.: Natural Gradient Approach to Blind Separation of Over- and Under-complete Mixtures. In: Proc. of Independent Component Analysis and Signal Separation, Aussois, France, pp. 455–460 (1999)Google Scholar
  15. 15.
    Zhang, L., Amari, S., Cichocki, A.: Equi-convergence Algorithm for blind separation of sources with arbitrary distributions. In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2085, pp. 826–833. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Geomatics EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Department of Civil EngineeringIndian Institute of TechnologyRoorkeeRoorkeeIndia

Personalised recommendations