Call for Paper - August 2019 Edition
IJCA solicits original research papers for the August 2019 Edition. Last date of manuscript submission is July 20, 2019. Read More

Satellite Image Compression using Kekre Wavelet Transform

Print
PDF
IJCA Proceedings on International Conference and Workshop on Emerging Trends in Technology 2014
© 2013 by IJCA Journal
ICWET2014 - Number 2
Year of Publication: 2013
Authors:
Vijaykumar P. Yele
Bijith Marakarkandy

Vijaykumar P.yele and Bijith Marakarkandy. Article: Satellite Image Compression using Kekre Wavelet Transform. IJCA Proceedings on International Conference and Workshop on Emerging Trends in Technology 2014 ICWET 2014(2):6-10, May 2013. Full text available. BibTeX

@article{key:article,
	author = {Vijaykumar P.yele and Bijith Marakarkandy},
	title = {Article: Satellite Image Compression using Kekre Wavelet Transform},
	journal = {IJCA Proceedings on International Conference and Workshop on Emerging Trends in Technology 2014},
	year = {2013},
	volume = {ICWET 2014},
	number = {2},
	pages = {6-10},
	month = {May},
	note = {Full text available}
}

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

  • D. Donoho, "Compressed sensing," IEEE Trans. Inform. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006.
  • Emmanuel Candes` and Justin Romberg," Sparsity and incoherence in compressive sampling"Published 10 April 2007 Online atstacks. iop. org/IP/23/969
  • Vivek K Goyal, et al"Compressive Sampling and Lossy Compression" IEEE SIGNAL PROCESSING AGAZINE,
  • MARCH 2008
  • Dr. H. B. Kekre et al, " Algorithm to Generate Kekre's Wavelet ransform from Kekre's Transform" International Journal of Engineering Science and Technology Vol. 2(5), 756-767, 2010
  • Dr. H. B. Kekre, Archana Athawale & Dipali adavarti "Algorithm to Generate Wavelet Transform from an Orthogonal Transform "International Journal Of Image Processing (IJIP), Volume (4): Issue (4) 444-455 2010
  • Multispectral Image Data Analysis System: (https://engineering. purdue. edu/~biehl/MultiSpec?)
  • Hyper spectral image test data set: (https;//www. ehu. es/ccwintco/index. php/Hyperspecral_remote_Sensing_Scence
  • Emmanuel J. Candès, "Compressed Sensing with Coherent and Redundant Dictionaries" Available on:http://arxiv. org/pdf/1005. 2613. pdf.
  • Emmanuel Candes` and Justin Romberg," Sparsity and incoherence incompressive sampling"Online atstacks. iop. org/IP/23/969, 2007.