Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

A Novel Adaptive Compression Technique for Dealing with Corrupt Bands and High Levels of Band Correlations in Hyperspectral Images Based on Binary Hybrid GA-PSO for Big Data Compression

Print
PDF
 
Download Full Text
International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 109 - Number 8
Year of Publication: 2015
Authors:
S. Kargozar Nahavandy
P. Ghamisi
L. Kumar
M. S. Couceiro
10.5120/19208-0915

S.Kargozar Nahavandy, P Ghamisi, L Kumar and M S Couceiro. Article: A Novel Adaptive Compression Technique for Dealing with Corrupt Bands and High Levels of Band Correlations in Hyperspectral Images Based on Binary Hybrid GA-PSO for Big Data Compression. International Journal of Computer Applications 109(8):18-25, January 2015. Full text available. BibTeX

@article{key:article,
	author = {S.Kargozar Nahavandy and P. Ghamisi and L. Kumar and M. S. Couceiro},
	title = {Article: A Novel Adaptive Compression Technique for Dealing with Corrupt Bands and High Levels of Band Correlations in Hyperspectral Images Based on Binary Hybrid GA-PSO for Big Data Compression},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {109},
	number = {8},
	pages = {18-25},
	month = {January},
	note = {Full text available}
}

Abstract

Hyperspectral sensors generate useful information about cli-mate and the earth's surface in numerous contiguous narrow spectral bands, being widely used in resource management, agriculture, environmental monitoring, among others. The compression of hyperspectral data helps in long-term storage and transmission systems. This paper introduces a new adap-tive compression method for hyperspectral data. The method is based on separating the bands with different specifications by the histogram analysis and Binary Hybrid Genetic Algorithm-Particle Swarm Optimization (BHGAPSO). The new proposed method improves the compression ratio of the best-known JPEG standards, saves storage space, and speeds up the transmission system. The proposed method is applied on two different test cases, and the results are evaluated and compared with a few powerful compression techniques, such as lossless JPEG and JPEG2000. The results confirm that the proposed method is accurate, simple and fast, which can be useful for big data (i. e, a high volume of data) processing.

References

  • M. R. Pickering and M. J. Ryan, hyperspectral data compression, G. Motta, F. Rizzo, and J. A. Storer, Eds. Springer, 2006, chapter 1.
  • J. Mielikainen and P. Toivanen, hyperspectral data com-pression, G. Motta, F. Rizzo, and J. A. Storer, Eds. Springer, 2006, chapter 2.
  • S. R. Tate, "Band ordering in lossless compression of multispectral images," IEEE Transaction on Computers, vol. 46, no. 4, pp. 477–483, 1997.
  • T. Ebrahimi, D. S. Cruz, J. Askelof, M. Larsson, and C. Christopoulos, "Jpeg 2000 still image coding versus other standards," in SPIE Int. Symposium, San Diego California USA, 30 Jul - 4 Aug 2000, invited paper in Special Session on JPEG2000.
  • P. Ghamisi, F. Sepehrband, J. Choupan, M. Mortazavi, "Binary Hybrid GA-PSO Based Algorithm for compres-sion of Hyperspectral Data", The 5th International Con-ference on Signal Processing and Communication Sys-tems (ICSPCS' 11), 12-14 December, Honolulu, Hawaii.
  • A. Skodras, C. Christopoulos, and T. Ebrahimi, "The JPEG2000 still image compression standard," IEEE Sig-nal Processing Magazine, pp. 36-58, sept 2001.
  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, Massachu-setts, USA: Addison–Wesley Longman.
  • J. Kennedy and R. C. Eberhart, Swarm Intelligence, Mor-gan Kaufmann Publishers, San Francisco, 2001.
  • P. Ghamisi, M. S. Couceiro, J. A. Benediktsson, A Novel Feature Selection Approach Based on FODPSO and SVM, IEEE Transaction on Geoscience and Remote Sensing, [In press].
  • P. Ghamisi, M. S. Couceiro, N. M. F. Ferreira, L. Kumar, "Use of Darwinian Particle Swarm Optimization technique for the segmentation of Remote Sensing images", IGARSS 2012, pp. 4295-4298, 22-27 July 2012.
  • K. Premalatha and A. M. Natarajan, Hybrid PSO and GA for Global Maximization, ICSRS Publication, Vol. 2, No. 4, December 2009
  • Russell C. Eberhart and YuhuiShi. Comparison between genetic algorithms and particle swarm optimization. In et. al. V. William Porto, editor, EvolutionaryProgramming, volume 1447 of Lecture Notes in Computer Science, pages 611–616. Springer, 1998.
  • Peter J. Angeline. Evolutionary optimization versus par-ticle swarm optimization: Philosophy and performance differences. In V. William Porto and et al. , editors, Evo-lutionary Programming, volume 1447 of Lecture Notes in Computer Science, pages 601–610. Springer, 1998.
  • T. Bushberg, The essential of Medical Imaging, 2nd ed. Philadelphia: Lippincott Williams & Wilkins, 2006.
  • R. Gonzales and R. Woods, Digital Image Processing, 3rd ed. New Jersey: Pearson Prentice Hall, Upper Saddle River, 2008, pp. 525-626.
  • P. Ghamisi, A. Mohammadzadeh, M. R. Sahebi, F. Sepehrband and J. Choupan, "A Novel Real Time Algo-rithm for Remote Sensing Lossless Data Compression based on Enhanced DPCM", International Journal of Computer Applications 27(1):47-53, August 2011.
  • F. Sepehrband, P. Ghamisi, A. Mohammadzadeh, M. R. Sahebi, J. Choupan, "Efficient Adaptive Lossless Com-pression of Hyperspectral Data Using Enhanced DPCM", International Journal of Computer Applications 35(4):6-11, December 2011.
  • F. Van den Bergh. An Analysis of Particle Swarm Opti-mizers, PhD Thesis. Department of Computer Science, University of Pretoria, South Africa, 2002.
  • P. Ghamisi, "A Novel Method for Segmentation of Re-mote Sensing Images based on Hybrid GA-PSO", Inter-national Journal of Computer Applications 29(2):7-14, September 2011.
  • P. Ghamisi, Micael S. Couceiro, Jon Atli Benediktsson, Nuno M. F. Ferreira "An Efficient Method for Segmenta-tion of Images Based on Fractional Calculus and Natural Selection", Expert Systems with Application Publisher, 39 (2012) 12407-12417.
  • Chun-Feng Lu and Chia-Feng Juang, Evolutionary fuzzy control of flexible AC transmission system, IEE Proc. -Gener. Transm. Distrib. , Vol. 152, No. 4, July 2005.
  • P. Ghamisi, J. A. Benediktsson, Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization, IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 309-313, Feb. 2015.
  • P. Ghamisi, "A Novel Method for Segmentation of Re-mote Sensing Images based on Hybrid GA-PSO", Inter-national Journal of Computer Applications, 29(2):7-14, September 2011. Published by Foundation of Computer Science, New York, USA.
  • P. Ghamisi, F. Sepehrband, L. Kumar, M. S. Couceiro, Fernando M. L. Martins, A New Method for Compres-sion of Remote Sensing Images Based on Enhanced Dif-ferential Pulse Code Modulation Transformation, Science Asia, 39 (5), 449-455.