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On Sphering the High Resolution Satellite Image Using Fixed Point Based ICA Approach

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Book cover Proceedings of International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 460))

Abstract

On sphering the satellite data, classified images are achieved by many authors that had tried to reduce the mixing effect in image classes with the help of different Independent component analysis (ICA) based approaches. In these cases multispectral images are limited with small spectral variation in heterogeneous classes. For better classification, high spectral variance among different classes and low spectral variance within a particular class should exhibit. In the consideration of this issue, a Fixed point (FP) based Independent Component Analysis (ICA) method is utilized to get better classification accuracy in the existing mixed classes that consist similar spectral behavior. This FP-ICA method identifies the objects from mixed classes having similar spectral characteristics, on sphering high resolution satellite images (HRSI). It also helps to reduce the effect of similar spectral behavior between different image classes. The estimation of independent component related to non-gaussian distribution data (image) with optimizing the performance of this approach with the help of nonlinearity, which utilize the low variance between similar spectral classes. It is quite robust, effortless in computation and high convergence rate, even though the spectral distributions of satellite images are rigid to classify. Hence, this FP-ICA approach plays a key role in image classification such as buildings, grassland area, road, and vegetation.

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References

  1. Amari, S.: Natural gradient work efficiently in learning. Neural Comput. 10(2), 251–276 (1998).

    Google Scholar 

  2. Cichocki, A., Unbehauen, R., Rummert, E.: Robust learning algorithm for blind separation of signals. Electronics Letters 30(17), 1386–1387 (1994).

    Google Scholar 

  3. Zhang, L., Amari, S., Cichocki, A.: Natural Gradient Approach to Blind Separation of Over- and Under-complete Mixtures. In Proceedings of ICA’99, Aussois, France, January 1999, pp. 455–460 (1999).

    Google Scholar 

  4. Zhang, L., Amari, S., Cichocki, A.: Equi-convergence Algorithm for blind separation of sources with arbitrary distributions. In Bio-Inspired Applications of Connectionism IWANN 2001. Lecture notes in computer science, vol. 2085, pp. 826–833 (2001).

    Google Scholar 

  5. Mohammadzadeh, A., ValadanZoej, M.J., Tavakoli, A.: Automatic main road extraction from high resolution satellite imageries by means of particle swarm optimization applied to a fuzzy based mean calculation approach. Journal of Indian society of Remote Sensing 37(2), 173–184 (2009).

    Google Scholar 

  6. Singh, P.P., Garg, R.D.: Automatic Road Extraction from High Resolution Satellite Image using Adaptive Global Thresholding and Morphological Operations. J. Indian Soc. of Remote Sens. 41(3), 631–640 (2013).

    Google Scholar 

  7. Benediktsson, J.A., Pesaresi, M., and 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, 1940–1949 (2003).

    Google Scholar 

  8. 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, 2080–2083 (2001).

    Google Scholar 

  9. Li, G., Wan,Y., Chen, C.: Automatic building extraction based on region growing, mutual information match and snake model. Information Computing and Applications, Part II, CCIS, vol. 106, pp. 476–483 (2010).

    Google Scholar 

  10. 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), 1340007(1–16) (2013).

    Google Scholar 

  11. Hyvarinen, A., Oja, E.: A Fast Fixed-Point Algorithm for Independent Component Analysis. Neural Computation 9(7), 1483–1492 (1997).

    Google Scholar 

  12. Hyvarinen, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13(4–5), 411-430 (2000).

    Google Scholar 

  13. Hyvarinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999).

    Google Scholar 

  14. Luenberger, D.: Optimization by Vector Space Methods, Wiley (1969).

    Google Scholar 

  15. Singh, P.P., Garg, R.D.: Fixed Point ICA Based Approach for Maximizing the Non-gaussianity in Remote Sensing Image Classification. Journal of Indian Society of Remote Sensing 43(4), 851–858 (2015).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Central Institute of Technology Kokrajhar, BTAD, Kokrajhar, Assam, India

    Pankaj Pratap Singh

  2. Geomatics Engineering Group, Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    R. D. Garg

Authors
  1. Pankaj Pratap Singh
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  2. R. D. Garg
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Corresponding author

Correspondence to Pankaj Pratap Singh .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Partha Pratim Roy

  4. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Debashis Sen

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Singh, P.P., Garg, R.D. (2017). On Sphering the High Resolution Satellite Image Using Fixed Point Based ICA Approach. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_37

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  • DOI: https://doi.org/10.1007/978-981-10-2107-7_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2106-0

  • Online ISBN: 978-981-10-2107-7

  • eBook Packages: EngineeringEngineering (R0)

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