Classification of Polari Metric SAR (POlSAR) Image Analysis using Decomposition Techniques

Print
IJCA Proceedings on National Conference on Digital Image and Signal Processing
© 2016 by IJCA Journal
NCDISP 2016 - Number 1
Year of Publication: 2016
Authors:
M. A. Shaikh
P. W. Khirade
S. B. Sayyad

M A Shaikh, P W Khirade and S B Sayyad. Article: Classification of Polari Metric SAR (POlSAR) Image Analysis using Decomposition Techniques. IJCA Proceedings on National Conference on Digital Image and Signal Processing NCDISP 2016(1):20-23, August 2016. Full text available. BibTeX

@article{key:article,
	author = {M. A. Shaikh and P. W. Khirade and S. B. Sayyad},
	title = {Article: Classification of Polari Metric SAR (POlSAR) Image Analysis using Decomposition Techniques},
	journal = {IJCA Proceedings on National Conference on Digital Image and Signal Processing},
	year = {2016},
	volume = {NCDISP 2016},
	number = {1},
	pages = {20-23},
	month = {August},
	note = {Full text available}
}

Abstract

The classification of Polarimetric Synthetic Aperture Radar (PolSAR) image using a different decomposition technique has become a very important task after availability of data. In this paper RADARSAT-2 C-band fully polarimetric SAR data is used. This data was in SLC (single look complex) format and was not geocoded. In the present paper different decomposition techniques applied on RADARSAT-2 data for city of British Columbia, Vancouver, Canada and later classified the data using various classification techniques like H-alpha, Wishart H-alpha and Wishart H-A-alpha. The PolSAR classified image analysis is done on the basis of image parameters like Mean, Median, Standard Deviation and Coefficient Variation. From the result, it is observed that the for classification analysis Wishart H-A-alpha classified image is better than H-alpha and Wishart H-alpha classified image.

References

  • T. M. Lillesand, R. W. Kiefer. 1999. Remote Sensing and Image Interpretation. 4th Edition, John Wiley & Sons, Inc.
  • Dong Y, Forester B, Ticehurst C. 1997. Radar Backscatter Analysis for Urban Environment. International Journal of Remote Sensing. 18, 6, 1352-1364.
  • R. Touzi, W. M. Boerner, J. S. Lee, and E. Lueneburg. 2004. A Review of Polarimetry in the Context of Synthetic Aperture Radar: Concepts and Information Extraction. Can. J. Remote Sensing. 30, 3, 380–407.
  • Xu-nan Liu, Bo Cheng. 2012. Polarimetric SAR Speckle Filtering for High-Resolution SAR Images Using RADARSAT-2 POLSAR SLC data. International Conference on Computer Vision in Remote Sensing (CVRS) 2012, 329 – 334.
  • S. B. Sayyad, P. W. Khirade, M. A. Shaikh. 2015. Speckle Filtering of Microwave C-Band Fine Quad Polarized RADARSAT-2 SAR Image Using Mean and Median Filter. Bionano Frontier. 8, 3, 294-297.
  • S. Cloude, E. Pottier. 1996. A Review of Target Decomposition Theorems in Radar Polarimetry. IEEE Transactions of Geoscience and Remote Sensing. 34, 2, 498-518.
  • S. R. Cloude and E. Pottier. 1997. An Entropy Based Classification Scheme for Land Applications of Polarimetric SAR. IEEE IGRS. 35, 1, 68-78.
  • M Ouarzeddine, and B Souissi. Unsupervised Classification Using Wishart Classifier. USTHB, F. E. I, BP No 32 EI Alia Bab Ezzouar, Alger.
  • J. S. Lee, M. R. Grunes, T. L. Ainsworth, L. J. Du, D. L. Schuler, S. R. Cloude. 1999. Unsupervised Classification Using Polarimetric Decomposition and Complex Wishart Distribution. IEEE Transactions Geoscience and Remote Sensing. 37/1, 5, 2249-2259.
  • RADARSAT-2 PolSAR Dataset Source-: http://gs. mdacorporation. com/SatelliteData/Radarsat2/ SampleDataset. aspx//