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Satellite Image Compression using Kekre Wavelet Transform

Published on May 2013 by Vijaykumar P.yele, Bijith Marakarkandy
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International Conference and Workshop on Emerging Trends in Technology 2014
Foundation of Computer Science USA
ICWET2014 - Number 2
May 2013
Authors: Vijaykumar P.yele, Bijith Marakarkandy
8990c37e-2e76-49fd-bf70-5d7e5d128293

Vijaykumar P.yele, Bijith Marakarkandy . Satellite Image Compression using Kekre Wavelet Transform. International Conference and Workshop on Emerging Trends in Technology 2014. ICWET2014, 2 (May 2013), 6-10.

@article{
author = { Vijaykumar P.yele, Bijith Marakarkandy },
title = { Satellite Image Compression using Kekre Wavelet Transform },
journal = { International Conference and Workshop on Emerging Trends in Technology 2014 },
issue_date = { May 2013 },
volume = { ICWET2014 },
number = { 2 },
month = { May },
year = { 2013 },
issn = 0975-8887,
pages = { 6-10 },
numpages = 5,
url = { /proceedings/icwet2014/number2/16536-1421/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology 2014
%A Vijaykumar P.yele
%A Bijith Marakarkandy
%T Satellite Image Compression using Kekre Wavelet Transform
%J International Conference and Workshop on Emerging Trends in Technology 2014
%@ 0975-8887
%V ICWET2014
%N 2
%P 6-10
%D 2013
%I International Journal of Computer Applications
Abstract

The evolution of satellite image technology is enabling the manipulation of a greater range of data contained in increasing types of satellite images. Efficient and effective utilization of transmission bandwidth and storage capacity have been a core area of research for remote sensing images. Hence image compression is required for multi-band satellite imagery. In addition, image quality is also an important factor after compression and reconstruction. The wavelet transform is anticipated to provide economical and informative mathematical representation of many objects of interest. In the proposed system, Kekres wavelet transform is used for compression of multispectral satellite image based on compressive sampling method. The compressed image performance is analyzed using Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR),Mean Square Error.

References
  1. D. Donoho, "Compressed sensing," IEEE Trans. Inform. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006.
  2. Emmanuel Candes` and Justin Romberg," Sparsity and incoherence in compressive sampling"Published 10 April 2007 Online atstacks. iop. org/IP/23/969
  3. Vivek K Goyal, et al"Compressive Sampling and Lossy Compression" IEEE SIGNAL PROCESSING AGAZINE,
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  10. Emmanuel Candes` and Justin Romberg," Sparsity and incoherence incompressive sampling"Online atstacks. iop. org/IP/23/969, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Compressive Sensing Incoherence measurement Matrix. Compression Ratio (cr) mean Square Error (mse) peak Signal To Noise Ratio (psnr)