Fast Encoding-Decoding of 3D Hyperspectral Images Using a Non-Supervised Multimodal Compression Scheme

Mourad Lahdir, Amine Nait-ali, Soltane Ameur

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DOI: 10.4236/jsip.2011.24045                

Abstract

We introduce in this paper an extension of the Multimodal Compression technique (MC) for the purpose of coding hyperspectral image sequences. The main idea requires few steps, namely: (1) reducing the size of the sequence by inserting smooth images containing less information into the remaining images of the same sequence, (2) then coding the new compacted sequence using 3D-SPIHT algorithm. In this new scheme, called MC-3D-SPIHT, the insertion is achieved only in the contour of each image, according to a non-supervised way, so that one can preserve the Region of Interest (ROI) quality. For this purpose, a mixing function is employed. After the decoding process, inserted images are extracted by a separation function and the original sequence is reconstructed. By considering data from AVIRIS database, we will show how one decrease significantly the computing time for both coding and decoding.

Keywords

Multimodal Compression, Hyperspectral Images, 3D-SPIHT

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	E. Christophe, C. Mailhes and P. Duhamel, “Hyperspectral Image Compression: Adapting SPIHT and EZW to Anisotropic 3D Wavelet Coding,” IEEE Transactions on Image Processing, Vol. 17, No. 12, 2008, pp. 2334-2346. doi:10.1109/TIP.2008.2005824
[2]	B. Penna, T. Tillo, E. Magli and G. Olmo, “Transform Coding Techniques for Lossy Hyperspectral Data Compression,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 5, 2007, pp. 1408-1421.
[3]	M. Lahdir, S. Ameur and A. Adane, “Algorithme non itératif basés sur les ondelettes biorthogonales et les fractales pour la compression des images satellitaires,” Télédétection, Vol. 6, No 4, 2006, pp. 345-360.
[4]	D. S. Taubman and M. W. Marcellin, “JPEG2000: Image Compression Fundamentals, Standards and Practice,” Kluwer Academic Publishers, Boston, 2002.
[5]	Q. Du and J. E. Fowler, “Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis,” IEEE Geoscience and Remote Sensing Letters, Vol. 4, No. 2, 2007, pp. 201-205. doi:10.1109/LGRS.2006.888109
[6]	P. L. Dragotti, P. Giovanni and A. R. P. Ragozini, “Compression of Multispectral Images by Three Dimensional SPIHT Algorithm,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 1, 2000, pp. 416-428. doi:10.1109/36.823937
[7]	J. E. Fowler and J. T. Rucker, “3D Wavelet-Based Compression of Hyperspectral Imagery,” In: C.-I. Chang, Ed., Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons, Inc., Hoboken, 2007.
[8]	A. Nait-Ali and C. Cavaro-Menard (Ed.), “Compression of Biomedical Images and Signals,” ISTE-John Wiley and Sons, London, 2008, pp. 247-275.
[9]	A. Nait-Ali, E. H. Zeybek and X. Drouot, “Introduction to Multimodal Compression of Biomedical Data,” In: A. Nait-Ali, Ed., Advanced Biosignal Processing, Springer, Berlin, 2009, pp. 353-375.
[10]	X. Tang and W. A. Pearlman, “Three-Dimensional Wavelet-Based Compression of Hyperspectral Images,” Chapter in Hyperspectral Data Compression, Kluwer Academic Publishers, Boston, 2005. http://www.cipr.rpi.edu/ pearlman
[11]	A. Said and 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, No. 3, 1996, pp. 243-250. doi:10.1109/76.499834
[12]	P. S. Yeh, G. Moury and P. Armbruster, “The CCSDS Data Compression Recommendation: Development and Status,” Proceedings of SPIE Application of Digital Image Processing, Seattle, 7-10 July 2002.
[13]	S.-E. Qian, J. Lévesque and R. A. Neville, “Evaluation of Noise Removal of Radiance Data Onboard Data Compression of Hyperspetral Imagery,” WSEAS International Conference on Remote Sensing, Venice, 2-4 November 2005, pp. 37-42.