CFP last date
20 January 2025
Reseach Article

Remote Sensing Image Compression using 3D-Oriented Wavelet Transform

by D. Napoleon, S. Sathya, M. Praneesh, M. Siva Subramanian
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 24
Year of Publication: 2012
Authors: D. Napoleon, S. Sathya, M. Praneesh, M. Siva Subramanian
10.5120/7119-9755

D. Napoleon, S. Sathya, M. Praneesh, M. Siva Subramanian . Remote Sensing Image Compression using 3D-Oriented Wavelet Transform. International Journal of Computer Applications. 45, 24 ( May 2012), 53-67. DOI=10.5120/7119-9755

@article{ 10.5120/7119-9755,
author = { D. Napoleon, S. Sathya, M. Praneesh, M. Siva Subramanian },
title = { Remote Sensing Image Compression using 3D-Oriented Wavelet Transform },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 24 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 53-67 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number24/7119-9755/ },
doi = { 10.5120/7119-9755 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:38:27.962549+05:30
%A D. Napoleon
%A S. Sathya
%A M. Praneesh
%A M. Siva Subramanian
%T Remote Sensing Image Compression using 3D-Oriented Wavelet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 24
%P 53-67
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Remote Sensing is simply defined as the observation of an object from some distance. By using the observation of object we can collect the information of an object without any physical contact with that object. We present a new technique for the compression of remote sensing images based on oriented wavelet transform. A 3D-Oriented Wavelet Transform (OWT) is introduced for efficient remote sensing image compression. To maximize the transform coding gain three separable 1D transforms are implemented in the same direction. This paper focus on compressing the remote sensing images based on 3D-OWT scheme and JPEG2000. Result show that our scheme with filters performs as well and better in lossless coding systems using 3D Oriented Wavelet Transform on remote sensing images.

References
  1. Chaudhuri and A. Samal, "An automatic bridge detection technique for multispectral images," IEEE Trans. Geosci. Remote Sens. , vol. 46, no. 9, pp. 2720–2727, Sep. 2008.
  2. Sirmacek and C. Unsalan, "Urban-area and building detection using SIFT keypoints and graph theory," IEEE Trans. Geosci. Remote Sens. , Vol. 47, no. 4, pp. 1156–1167, Apr. 2009.
  3. B. Penna, T. Tillo, E. Magli, and G. Olmo, "Transform coding techniques for lossy hyperspectral data compression," IEEE Trans. Geosci. Remote Sens. , vol. 45, no. 5, pp. 1408–1421, May 2007.
  4. D. Taubman and M. Marcellin, JPEG2000: Image Compression Fundamentals, Standards and Practice. Norwell, MA: Kluwer, 2001.
  5. D. Taubman, "High-performance scalable image compression with EBCOT," IEEE Trans. Image Process. , vol. 9, no. 7, pp. 1158–1170, Jul. 2000.
  6. A. Zandi, J. D. Aallen, E. L. Schwartz and M. Boliek, "CREW: Compression with reversible embedded wavelets", Proc. of IEEE Data Compression Conference, Snowbird, Vtah, pp. 212-221, March 1995.
  7. M. Boliek, M. Gormish, E. L. Schwartz and A. F. Keith, "Decoding Compression with Reversible Embedded Wavelets (CREW) Codestreams", Journal of Electronic Imaging, Vol. 7, no. 3, pp. 402-409, July 1998.
  8. Barbara Penna, Tammam Tillo, IEEE,Enrico Magli, Gabriella Olmo, "Transform coding techniques for lossy hyperspectral data compression", IEEE Transactions On Geoscience And Remote Sensing, Vol. 45, No. 5, May 2007.
  9. Vivien Chappelier, Christine Guillemot, "Oriented Wavelet Transform for Image Compression and Denoising", IEEE Transactions On Image Processing,
  10. A. Bovik, Handbook of Image and Video Processing, 2nd ed. Singapore: Elsevier, 2006.
  11. S. Mallat, "Multifrequency channel decompositions of images and wavelet models," IEEE Trans. Acoust. , Speech, Signal Processing, vol. 37, pp. 2091–2110, Dec. 1989.
  12. L. Yaroslavsky, "Signal sin-interpolation: A fast computer algorithm," Bioimaging, vol. 4, no. 4, pp. 225–231, Dec. 1996.
  13. L. Yaroslavsky and Y. Chernobrodov, "DFT and DCT based discrete sincinterpolation methods for direct Fourier tomographic reconstruction," in Proc. 3rd Int. Symp. Image Signal Process. Anal. , 2003, pp. 405–410.
  14. L. Yaroslavsky and M. Eden, Fundamentals of Digital Optics. Boston, MA: Birkhauser, 1996.
  15. L. Yaroslavsky, "Fast signal sinc-interpolation methods for signal and image resampling," in Proc. SPIE—Image Processing: Algorithms and Systems, 2002, vol. 4667, pp. 120–129.
  16. Daubechies and W. Sweldens, "Factoring wavelet transforms into lifting steps," in J. Fourier Anal. Appl. , vol. 4(3), 1998, pp. 247–269.
  17. W. Sweldens, "The lifting scheme: A construction of second generation wavelets," SIAM J. Math. Anal. , vol. 29, no. 2, pp. 511–546, 1997.
  18. James S. Walker, "Wavelet-based Image Compression", Transforms and Data Compression.
  19. Wang, Z. ; Bovik, A. C. ; Lu, L. , (2002). Why is Image Quality Assessment So Difficult? IEEE International Conference on Acoustics, Speech, & Signal Processing, 4, pp. IV-3313 - IV-3316.
  20. Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, And Eero P. Simoncelli, "Image Quality Assessment: From Error Measurement To Structural Similarity", IEEE Transactions On Image Processing, Vol. 13, No. 1, January 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Remote Sensing Image Image Compression 3d-owt Jpeg2000