CFP last date
15 January 2025
Call for Paper
February Edition
IJAIS solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 15 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

Performance of Wavelets for Information Preservation in Hyperspectral Image Compression

by Sonal S. Save, R. R. Sedamkar
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 2
Year of Publication: 2015
Authors: Sonal S. Save, R. R. Sedamkar
10.5120/ijais15-451370

Sonal S. Save, R. R. Sedamkar . Performance of Wavelets for Information Preservation in Hyperspectral Image Compression. International Journal of Applied Information Systems. 9, 2 ( June 2015), 11-16. DOI=10.5120/ijais15-451370

@article{ 10.5120/ijais15-451370,
author = { Sonal S. Save, R. R. Sedamkar },
title = { Performance of Wavelets for Information Preservation in Hyperspectral Image Compression },
journal = { International Journal of Applied Information Systems },
issue_date = { June 2015 },
volume = { 9 },
number = { 2 },
month = { June },
year = { 2015 },
issn = { 2249-0868 },
pages = { 11-16 },
numpages = {9},
url = { https://www.ijais.org/archives/volume9/number2/755-1370/ },
doi = { 10.5120/ijais15-451370 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:59:45.719639+05:30
%A Sonal S. Save
%A R. R. Sedamkar
%T Performance of Wavelets for Information Preservation in Hyperspectral Image Compression
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 9
%N 2
%P 11-16
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper investigates various wavelets in terms of information preservation in hyperspectral image analysis. The compression method uses Principal Component Analysis (PCA) to provide spectral decorrelation and also dimensionality reduction. Principal Component (PC) images are then compressed by various wavelets and Set Partitioning in Hierarchical Trees (SPIHT) based coder. Experimental results by using five wavelets show that the compression method preserves spatial details and spectral features for all wavelets. Among the five wavelets used, coiflet achieves higher signal-to-noise ratio at high compression in spectral dimension. Performance is best when a few (10 or less than 10) PCs are retained for coding. The order of performance is coiflet2, biorthogonal2. 2, symlet2, daubechies4 and biorthogonal1. 1 for given AVIRIS dataset.

References
  1. X. Tang, W. Pearlman: Three Dimensional Wavelet Based Compression of Hyperspectral Images. In Hyperspectral Data Compression, pp 273-308, Springer.
  2. J. E. Fowler, J. T. Rucker, "3D Wavelet based Compression of Hyperspectral Imagery", In Hyperspectral Data Exploitation: Theory and Applications, Chapter 14, John Wiley& sons Inc, 2007.
  3. Qian Du, and James E. Fowler, "Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis", IEEE Geosci. Remote Sens. Lett. , pp. 201-205, April 2007
  4. S. E. Qian, A. B. Hollinger, S. Williams, and D. Manak, "Vector quantization using spectral index based multiple subcodebooks for hyperspectral data compression", IEEE transactions on Geoscience and Remote Sensing, vol. 38, no. 3, pp. 1183-1190, May 2000.
  5. B. M. Schmanske, M. H. Loew, "Effect of Lossy Wavelet based Compression on Classification Accuracy", IEEE 0-7803-7031-7/01, 2001
  6. B. Penna, T. Tillo, E. Magli and G. Olmo, "Transform coding techniques for lossy hyperspectral data compression", ", IEEE Transaction on geoscience and remote sensing, vol. 45, no. 5, May 2007.
  7. I. Blanes, J. Serra-Sagrista, " Cost and scalability improvements to the Karhunen-Loeve Transform for remote sensing image coding", IEEE Transaction on geoscience and remote sensing, vol. 48, no. 7, July 2010
  8. Y. Feng, Jiakai Lv, J. Su, "Feature preserving compression for hyperspectral remote sensing images", ICIEA 978-1-4244-2800-7/09, IEEE 2009
  9. E Christophe, C Mailhes, P Duhamel, "Hyperspectral Image Compression: Adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding", IEEE Transactions on Image Processing, vol. 17, No. 12, December 2008
  10. J. Burger, A. Gowen, "Data handling in hyperspectral image analysis", in Chemometrics and Intelligent Laboratory Systems, Elsevier 2011,pp. 13-22.
  11. Gonzales, Woods, Eddins Digital Image Processing Using MATLAB 2nd edition, Tata McGraw Hill, 2012
  12. J. F. Scholl, E. L. Dereniak, "Higher-dimensional wavelet transforms for hyperspectral data compression of Data/Image Coding, Compression and feature recognition", in: M. S. Schmalz (Ed. ), Mathematics and Encryption VI with Applications, Proceedings of SPIE, Volume 5208, 2003, pp. 129–140
  13. J. M. Shapiro, "Embedded image coding using zerotrees of wavelet coefficients", IEEE Trans. Signal Process. Vol. 41, no. 12, pp. 3445-3462, Dec1993.
  14. A. Said, W. A. Pearlman, "A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees", IEEE Transactions on circuits and systems for video technology, vol. 6, June 1996
  15. John Serra-Sagrista, F A Llinas : Remote Sensing Data Compression. In Computational Intelligence for Remote Sensing, pp 27-61, Springer
Index Terms

Computer Science
Information Sciences

Keywords

Dimensionality reduction Information preservation Spectral angle mapper covariance matrix

Powered by PhDFocusTM