Random Articles
Reseach Article
Efficient Adaptive Lossless Compression of Hyperspectral Data using Enhanced DPCM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 35 - Number 4 |
| Year of Publication: 2011 |
| Authors: Farshid Sepehrband, Pedram Ghamisi, Ali Mohammadzadeh, Mohammad Reza Sahebi, Jeiran Choupan |
10.5120/4386-6078
|
Farshid Sepehrband, Pedram Ghamisi, Ali Mohammadzadeh, Mohammad Reza Sahebi, Jeiran Choupan . Efficient Adaptive Lossless Compression of Hyperspectral Data using Enhanced DPCM. International Journal of Computer Applications. 35, 4 ( December 2011), 6-11. DOI=10.5120/4386-6078
Abstract
Hyperspectral sensors are imaging spectrometry sensors that generate useful information about climate and the earth surface in numerous contiguous narrow spectral bands, and are widely used in resource management, agriculture, environmental monitoring, etc. Compression of the Hyperspectral data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as Hyperspectral data. There are a few well-known methods for lossless compression, such as JPEG standards, and some other previously proposed methods. However, improving the compression ratio of previous methods is the major focus in Hyperspectral-data compression. This paper introduces two new lossless compression methods. One of these methods is adaptive and powerful for the compression of Hyperspectral data, which is based on separating the bands with different specifications and compressing each one efficiently. The new proposed methods improve the compression ratio of the JPEG standards, save storage space, and speed up the transmission system. The proposed methods are applied on different test cases, and the results are evaluated and compared with other state-of-the-art compression methods, such as lossless JPEG and JPEG2000.
References
- Manolakis, D. Marden, and G. Shaw, “Hyperspectral image processing for automatic target detection ap- plications,” Lincoln Laboratory Journal, vol. 14, no. 1, 2003.
- S. R. Tate, “Band ordering in lossless compression of multispectral images,” IEEE Trans. on Com., vol. 46, no. 4, pp. 477–483, 1997.
- J.Mielikainen and P.Toivanen, hyperspectral data compression, G. Motta, F. Rizzo, and J. A.Storer, Eds. Springer, 2006, chapter 2.
- M. R.Pickering and M. J.Ryan, hyperspectral data compression, G. Motta, F. Rizzo, and J. A.Storer, Eds. Springer, 2006, chapter 1.
- 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.
- A. Skodras, C. Christopoulos, and T. Ebrahimi, “The jpeg2000 still image compression standard,” IEEE Signal Proc. Mag., pp. 36–58, sept 2001.
- E. Magli, G. Olmo, and E. Quacchio, “Optimized onboard lossless and near-lossless compression of hyper- spectral data using calic,” IEEE Geosci. Remote Sens. Lett., vol. 1, no. 1, pp. 21–25, Jan 2004.
- B. Aiazzi, L. Alparone, S. Baronti, and C. Lastri, “Crisp and fuzzy adaptive spectral predictions for lossless and near-lossless compression of hyperspectral imagery,” IEEE Geosci. Remote Sens. Lett., vol. 4, no. 4, p.532-536, Oct 2007.
- J. Mielikainen and P. Toivanen, “Lossless compression of hyperspectral images using a quantized index to lookup tables,” IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, p. 474478, Jul 2008.
- F. Sepehrband, J. Choupan, and M. Mortazavi, “Simple lossless and near-lossless medical image compression based on enhanced DPCM transformation,” in PacRim, Victoria, BC, Canada 2011.
- P. Ghamisi, A. Mohammadzadeh, M. Sahebi, F. Sepehrband, and J. Choupan, “A novel real time algorithm for remote sensing lossless data compression based on enhanced dpcm,” International Journal of Computer Applications, vol. 27, no. 1, pp. 47–53, August 2011.
- R. Gonzales and R. Woods, Digital Image Processing, 3rd ed. New Jersey: Pearson Prentice Hall, Upper Saddle River, 2008, pp. 525-626.
Index Terms
Keywords