Group and Region Based Parallel Compression Method Using Signal Subspace Projection and Band Clustering for Hyperspectral Imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 4, Issue 3, 2011, Pages 565-578

  Chang, Lena ^a   Chang, Yang Lang ^b   Tang, Z S ^a   Huang, Bormin ^c  

^a NATIONAL TAIWAN OCEAN UNIVERSITY   (Taiwan)

^b NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Clustering signal subspace projection (CSSP); hyperspectral image compression; maximum correlation band clustering (MCBC); principal components analysis (PCA)

Indexed keywords

COMPRESSION APPROACH; COMPRESSION METHODS; COMPUTATION EFFICIENCY; COMPUTING EFFICIENCY; COMPUTING TECHNIQUES; DEGREE CORRELATION; HIGH-DIMENSIONAL IMAGES; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGE COMPRESSION; HYPERSPECTRAL IMAGERY; IMAGE DATA; IMAGE REGIONS; JPEG 2000; MAXIMUM CORRELATIONS; PARALLEL CLUSTERS; PRINCIPAL COMPONENTS ANALYSIS; REGION BASED APPROACH; REGION-BASED; SIGNAL SUB-SPACE; SIMULATION RESULT; SPECTRAL BAND;

CLUSTER ANALYSIS; CLUSTER COMPUTING; CLUSTERING ALGORITHMS; COMPRESSION RATIO (MACHINERY); COMPUTER SOFTWARE SELECTION AND EVALUATION; DIGITAL IMAGE STORAGE; IMAGE COMPRESSION; INTERFERENCE SUPPRESSION; METADATA; MONOLITHIC MICROWAVE INTEGRATED CIRCUITS; PARALLEL ARCHITECTURES; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SIGNAL ANALYSIS;

THREE DIMENSIONAL;

ALGORITHM; AVIRIS; COMPUTER SIMULATION; IMAGE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS;

EID: 80052317866     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2011.2162091     Document Type: Article

References (36)

1. J. A. Saghri, A. G. Tescher, and J. T. Reagan, “Practical transform coding of multispectral imagery,” IEEE Signal Process. Mag., vol. 12, pp. 32–43, Jan. 1995.
2. J. Lee, “Optimized quadtree for Karhunen-Loeve transform in multispectral image coding,” IEEE Trans. Image Process., vol. 8, no. 4, pp. 453–461, Apr. 1999.
3. G. R. Canta and G. Poggi, “Compression of multispectral images by address-predictive vector quantization,” Signal Process.: Image Commun., vol. 11, pp. 147–159, Dec. 1997.
4. G. R. Canta and G. Poggi, “Kronecker-product gain-shape vector quantization for multispectral and hyperspectral image coding,” IEEE Trans. Image Process., vol. 7, pp. 668–678, May 1998.
5. L. Chang, “Multispectral image compression using eigen-region-based segmentation,” Pattern Recognition, vol. 37, no. 6, pp. 1233–1243, 2004.
6. B. R. Epstein, R. Hingorani, J. M. Shapiro, and M. Czigler, “Multispectral KLT-wavelet data compression for landsat thematic mapper images,” in Proc. Data Compression Conf., 1992, pp. 200–208.
7. A. Said and W. A. Pearlman, “A new, fast, and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. Video Technol., vol. 6, no. 3, pp. 243–350, Jun. 1996.
8. P. L. Dragotti, G. Poggi, and A. R. P. Ragozini, “Compression of multispectral images by three dimensional SPIHT algorithm,” IEEE Trans. Geosci. Remote Sens., vol. 38, pp. 416–428, Jan. 2000.
9. G. P. Abousleman, M. W. Marcellin, and B. R. Hunt, “Hyperspectral image compression using 3D discrete cosine transform and entropy-constrained trellis-coded quantization,” in Proc. SPIE Opt. Eng., 1994, pp. 136–147.
10. X. Tang, W. A. Pearlman, and J. W. Modestino, “Hyperspectral image compression using three-dimensional image coding,” in Proc. SPIE, Jan. 2003, vol. 5022.
11. W. A. Pearlman, A. Islam, N. Nagaraj, and A. Said, “Efficient, low-complexity image coding with a set partitioning embedded block coder,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, pp. 1219–1235, 2004.
12. B.-J. Kim, Z. Xiong, and W. A. Pearlman, “Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT),” IEEE Trans. Circuits Syst. Video Technol., vol. 10, no. 8, pp. 1374–1387, Dec. 2000.
13. X. Tang, S. Cho, and W. A. Pearlman, “3D set partitioning coding methods in hyperspectral image compression,” in Proc. ICIP, Sep. 2003, vol. 2, pp. 239–242.
14. D. S. Taubman and M. W. Marcellin, JPEG2000 Image Compression: Fundamentals, Standards and Practice. Boston, MA: Kluwer Academic, 2002.
15. J. T. Rucker, J. E. Fowler, and N. H. Younan, “JPEG2000 coding strategies for hyperspectral data,” in Proc. Int. Geoscience and Remote Sensing Symp. (IGARSS'05), Jul. 2005, vol. 1, pp. 128–131.
16. B. Penna, T. Tillo, E. Magli, and G. Olmo, “Progressive 3D coding of hyperspectral images based on JPEG 2000,” IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 125–129, Jan. 2006.
17. J. E. Fowler and J. T. Rucker, “3D wavelet-based compression of hyperspectral imagery,” in Hyperspectral Data Exploitation: Theory and Applications, C.-I. Chang, Ed. Hoboken, NJ: Wiley, 2007.
18. D. Qian and E. James, “Hyperspectral image compression using JPEG2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett., vol. 4, pp. 201–205, April 2007.
19. L. Chang, Y.-L. Chang, and C.-M. Cheng, “An efficient hierarchical hyperspectral image classification using binary quatemion-moment-preserving thresholding technique,” in Proc. Int. Geoscience and Remote Sensing Symp. (IGARSS'09), Jul. 2009, vol. 2, pp. 294–297.
20. L. Chang, C. M. Chen, and J. D. Chen, “A novel segmentation technique using eigen space projection for satellite image indexing,” in Proc. Int. Geoscience and Remote Sensing Symp. (IGARSS'04), Jul. 2004, vol. 3, pp. 1573–1576.
21. W. H. Tsai, “Moment-preserving thresholding: A new approach,” Computer Vision, Graphics, and Image Processing, vol. 29, no. 3, pp. 377–393, 1985.
22. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithm, 2nd ed. New York: The MIT Press, 2001.
23. D. A. Marsaikhan and M. Gangoring, “Different approaches in nature extraction for hyperspectral image classification,” Institute of Informatics and R.S, Mongolian Academy of Science, Av. Enkhataivan-54B, Ulaanbaatar-51, Mongolia, ACRS, 1999.
24. Y. L. Chang and C. C. Han et al., “A modular eigenspace scheme for high dimensional data classification,” Future Generation Computer Systems, pp. 1131–1143, 2004.
25. Matlab Distributed Computing Engine (MDCE). [Online]. Available: http://www.mathworks.com/products/distriben/
26. MATLAB Parallel Computing Toolbox (PCT). [Online]. Available: http://www.mathworks.com/products/parallel-computing/
27. Q. Du, W. Zhu, H. Yang, and J. E. Fowler, “Segmented principal component analysis for parallel compression of hyperspectral imagery,” IEEE Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 713–717, Oct. 2009.
28. R. Gaetano, G. Scarpa, and G. Poggi, “Hierarchical texture-based segmentation of multiresolution remote-sensing images,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2129–2147, Jul. 2009.
29. J. Mielikainen and P. Toivanen, “Clustered DPCM for the lossless compression of hyperspectral images,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 12, pp. 2943–2946, Dec. 2003.
30. 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, pp. 474–478, Jul. 2008.
31. O. M. Kosheleva, B. E. Usevitch, S. D. Cabrera, and E. Vidal, Jr., “Rate distortion optimal bit allocation methods for volumetric data using JPEG 2000,” IEEE Trans. Image Process., vol. 15, no. 8, pp. 2106–2112, Aug. 2006.
32. B. E. Usevitch, “A tutorial on modem lossy wavelet image compression: Foundations of JPEG2000,” IEEE Signal Process. Mag., vol. 18, pp. 22–35, Sep. 2001.
33. A. Lucero, S. D. Cabrera, A. Aguirre, E. Vidal, Jr., and Miguel Argaez, “Comparison of bit allocation methods for compressing three-dimensional meteorological data after applying klt,” in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS'04), Anchorage, Alaska, Sep. 2004.
34. Ra Documentation, Image Processing Toolbox User's Guide. [Online]. Available: http://www.mathworks.com/help/toolbox/images/
35. X. Tang and W. A. Pearlman, “Hyperspectral data compression,” in Three-Dimensional Wavelet-Based Compression of Hyperspectral Images. Boston, MA: Kluwer Academic, 2006.
36. G. Motta and J. Storer, “11 spectral/spatial hyperspectral image compression,” in Hyperspectral Data Compression. New York: Springer, 2006.