The promise of reconfigurable computing for hyperspectral imaging onboard systems: A review and trends

Proceedings of the IEEE

Volumn 101, Issue 3, 2013, Pages 698-722

  Lopez, Sebastian ^a   Vladimirova, Tanya ^b   Gonzalez, Carlos ^c   Resano, Javier ^d   Mozos, Daniel ^c   Plaza, Antonio ^e  

^a UNIVERSITY OF LAS PALMAS DE GRAN CANARIA   (Spain)

^b UNIVERSITY OF LEICESTER   (United Kingdom)

^c UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

^d UNIVERSITY OF ZARAGOZA   (Spain)

^e UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

Field programmable gate arrays (FPGAs); hyperspectral data compression; hyperspectral remote sensing; reconfigurable hardware; spectral unmixing

Indexed keywords

COMPUTER HARDWARE; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); GREEN COMPUTING; IONIZING RADIATION; LOGIC GATES; RECONFIGURABLE ARCHITECTURES; RECONFIGURABLE HARDWARE; REMOTE SENSING; SPACE OPTICS; SPECTRAL RESOLUTION; SPECTROSCOPY;

CONNECTION REQUIREMENTS; HIGH PERFORMANCE COMPUTING SYSTEMS; HIGH SPECTRAL RESOLUTION; HYPERSPECTRAL DATA; HYPERSPECTRAL REMOTE SENSING; RECONFIGURABLE COMPUTING; SPECTRAL UNMIXING; TECHNOLOGICAL ASPECTS;

HYPERSPECTRAL IMAGING;

EID: 84874542998     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/JPROC.2012.2231391     Document Type: Review

References (99)

1. R. Trautner, "ESA's roadmap for next generation payload data processors," in Proc. DASIA Conf., 2011. [Online]. Available: http://www.esa.int/TEC/OBDP/.
2. Zynq-7000 All Programmable SoC. [Online]. Available: http://www.xilinx. com/products/silicon-devices/soc/zynq-7000/index.htm
3. Altera SoC FUGA. [Online]. Available: http://www.altera.com/devices/ processor/soc-fpga/proc-soc-fpga.html
4. S. Hauck and A. Dehon, Eds."Device architecture," in Reconfigurable Computing. Amsterdam, The Netherlands: Elsevier, 2008, pp. 3-27.
5. Space-Grade Virtex-4QV FUGA. [Online]. Available: http://www.xilinx.com/ products/silicon-devices/fpga/virtex-4qv/index.htm
6. Space-Grade Virtex-5QV FUGA. [Online]. Available: http://www.xilinx.com/ products/silicon-devices/fpga/virtex-5qv/index.htm
7. Reports on Radiation Effects in Xilinx FPGAs. [Online]. Available: http://parts. jpl.nasa.gov/organization/group-5144/radiation-effects-in-fpgas/ xilinx/
8. ProASIC3 FPGA. [Online]. Available: http://www.actel.com/products/pa3/
9. T. Kuwahara, "FPGA-based reconfigurable on-board computing systems for space applications," Ph.D. dissertation, Faculty Aerosp. Eng. Geodesy, Univ. Stuttgart, Stuttgart, Germany, 2009.
10. A. Plaza, Q. Du, Y.-L. Chang, and R. L. King, "High performance computing for hyperspectral remote sensing," IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., vol. 4, no. 3, pp. 528-544, Sep. 2011.
11. A. Plaza and C.-I. Chang, High Performance Computing in Remote Sensing. Boca Raton, FL: CRC Press, 2007.
12. A. Plaza, D. Valencia, and J. Plaza, "High-performance computing in remotely sensed hyperspectral imaging: The pixel purity index algorithm as a case study," in Proc. Int. Parallel Distrib. Process. Symp. (IPDPS), 2006.
13. A. Plaza, J. Plaza, A. Paz, and S. Sanchez, "Parallel hyperspectral image and signal processing," IEEE Signal Process. Mag., vol. 28, no. 3, pp. 119-126, May 2011.
14. C. A. Lee, S. D. Gasster, A. Plaza, C.-I. Chang, and B. Huang, "Recent developments in high performance computing for remote sensing: A review," IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., vol. 4, no. 3, pp. 508-527, Sep. 2011.
15. G. Yu, T. Vladimirova, and M. N. Sweeting, "Image compression systems on board satellites," Acta Astronautica, vol. 64, no. 9-10, pp. 988-1005, May 2009.
16. N. S. Jayant and P. Noll, Digital Coding of Waveforms: Principles and Applications to Speech and Video. Englewood Cliffs, NJ: Prentice-Hall, 1984.
17. M. Rabbani and P. W. Jones, Digital Image Compression Techniques. Bellingham, WA: SPIE Press, 1991.
18. B. V. Brower, D. Couwenhoven, B. Gandhi, and C. Smith, "ADPCM for advanced LANDSAT downlink applications," in Conf. Rec. 27th Asilomar Conf. Signals Syst. Comput., 1993, vol. 2, pp. 1338-1341.
19. Lossless Data Compression, Recommendation for Space Data System Standards, CCSDS 121.0-B-1, May 1997.
20. W. B. Pennebaker and J. L. Mitchell, JPEG Still Image Data Compression Standard. London, U.K.: Chapman & Hall, 1993.
21. M. J. Weinberger, G. Seroussi, and G. Sapiro, "The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS," IEEE Trans. Image Process., vol. 9, no. 8, pp. 1309-1324, Aug. 2000.
22. D. S. Taubman and M. W. Marcellin, JPEG2000: Image Compression Fundamentals, Standard and Practice. Norwell, MA: Kluwer, 2002.
23. J. M. Shapiro, "Embedded image coding using zerotrees of wavelet coefficients," IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3445-3462, Dec. 1993.
24. 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. 243-250, Jun. 1996. (Pubitemid 126777405)
25. Image Data Compression, Recommendation for Space Data System Standards, CCSDS 122.0-B-1, CCSDS (2005), Nov. 2005.
26. P.-S. Yeh and J. Venbrux, "A high performance image data compression technique for space applications," in Proc. NASA Earth Sci. Technol. Conf., 2003, pp. 214-228.
27. J. Serra-Sagrista, C. Fernandez-Cordoba, F. Auli-Llinas, F. Garcia-Vilchez, and J. Minguillon, "Lossy coding techniques for high resolution images," Proc. SPIEVInt. Soc. Opt. Eng., vol. 5238, pp. 276-287, 2004.
28. J. Serra-Sagrista, F. Auli-Llinas, F. Garcia-Vilchez, and C. Fernandez-Cordoba, "Review of CCSDS-ILDC and JPEG2000 coding techniques for remote sensing," Proc. SPIEVInt. Soc. Opt. Eng., vol. 5573, pp. 250-261.
29. Y. Pen-Shu, P. Armbruster, A. Kiely, B. Masschelein, G. Moury, C. Schaefer, and C. Thiebaut, "The new CCSDS image compression recommendation," in Proc. IEEE Conf. Aerosp., 2005, pp. 4138-4145.
30. N. R. Mat Noor and T. Vladimirova, "Investigation into lossless hyperspectral image compression for satellite remote sensing," Int. J. Remote Sens., to be published.
31. G. Motta, F. Rizzo, and J. A. Storer, Eds. "Lossless predictive compression of hyperspectral images," in Hyperspectral Data Compression. New York: Springer-Verlag, 2005.
32. M. J. Ryan and J. F. Arnold, "The lossless compression of AVIRIS images by vector quantization," IEEE Trans. Geosci. Remote Sens., vol. 35, no. 3, pp. 546-550, May 1997. (Pubitemid 127771093)
33. M. J. Ryan and J. F. Arnold, "Lossy compression of hyperspectral data using vector quantization," Remote Sens. Environ., vol. 61, no. 3, pp. 419-436, Sep. 1997. (Pubitemid 28052031)
34. G. Motta, F. Rizzo, and J. A. Storer, Eds. "An architecture for the compression of hyperspectral imagery," in Hyperspectral Data Compression. New York: Springer-Verlag, 1995.
35. J. A. Saghri, A. G. Tescher, and J. T. Reagan, "Practical transform coding of multispectral imagery," IEEE Signal Process. Mag., vol. 12, no. 1, pp. 32-43, Jan. 1995.
36. G. Liu and F. Zhao, "Efficient compression algorithm for hyperspectral images based on correlation coefficients adaptive 3D zerotree coding," IET Image Process., vol. 2, no. 2, pp. 72-82, Apr. 2008. (Pubitemid 351598719)
37. I. Blanes and J. Serra-Sagrista, "Clustered reversible-KLT for progressive lossy-to-lossless 3D image coding," in Proc. Data Compression Conf., 2009, pp. 233-242.
38. Lossless Multispectral & Hyperspectral Image CompressionVRecommended Standard, CCSDS 123.0-B-1, May 2012.
39. Lossless Data CompressionVRecommended Standard, CCSDS 121.0-B-2, May 2012.
40. Y.-T. Hwang, C.-C. Lin, and R.-T. Hung, "Lossless hyperspectral image compression system-based on HW/SW codesign," IEEE Embedded Syst. Lett., vol. 3, no. 1, pp. 20-23, Mar. 2011.
41. N. Aranki, D. Keymeulen, A. Bakhshi, and M. Klimesh, "Hardware implementation of lossless adaptive and scalable hyperspectral data compression for space," in Proc. NASA/ESA Conf. Adapt. Hardware Syst., 2009, pp. 315-322.
42. G. Yu, T. Vladimirova, and M. N. Sweeting, "FPGA-based on-board multi/hyperspectral image compression system," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2009, vol. 5, pp. 212-215.
43. A. C. Miguel, A. R. Askew, A. Chang, S. Hauck, R. E. Ladner, and E. A. Riskin, "Reduced complexity wavelet-based predictive coding of hyperspectral images for FPGA implementation," in Proc. Data Compression Conf., 2004, pp. 469-478.
44. D. Valencia and A. Plaza, "FPGA-based hyperspectral data compression using spectral unmixing and the pixel purity index algorithm," in Proc. 6th Int. Conf. Comput. Sci., 2006, pp. 888-891. (Pubitemid 44152757)
45. A. Said and W. A. Pearlman, "A new, fast, and efficient image codec based on set partitioning in hierarchical trees," IEEE Trans. Circuits Syst., vol. 6, no. 3, pp. 243-250, Jun. 1996. (Pubitemid 126777405)
46. A. Plaza, "Towards real-time compression of hyperspectral images using Virtex-II FPGAs," in Proc. Int. Euro-Par Conf., 2007, pp. 248-257.
47. A. Plaza, S. Sanchez, A. Paz, and J. Plaza, "GPUs versus FPGAs for onboard compression of hyperspectral data," in Proc. Int. Workshop On-Board Payload Data Compression, 2010, pp. 318-324.
48. S. Deo, "Power consumption calculation of AP-DCD algorithm using FPGA platform," in Proc. Int. Conf. Reconfigurable Comput. FPGAs, 2010, pp. 388-393.
49. A. Plaza, J. A. Benediktsson, J. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, J. A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol. 113, no. supplement 1, pp. 110-122, Sep. 2009.
50. J. M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot, "Hyperspectral unmixing overview: Geometrical, statistical and sparse regression-based approaches," IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 354-379, Apr. 2012.
51. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 650-663, Mar. 2004.
52. J. M. P. Nascimento and J. M. Bioucas-Dias, "Does independent component analysis play a role in unmixing hyperspectral data?" IEEE Trans. Geosci. Remote Sens., vol. 43, no. 1, pp. 175-187, Jan. 2005. (Pubitemid 40162736)
53. D. Heinz and C.-I. Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 3, pp. 529-545, Mar. 2001. (Pubitemid 32400422)
54. M. Velez-Reyes, A. Puetz, M. P. Hoke, R. B. Lockwood, and S. Rosario, "Iterative algorithms for unmixing of hyperspectral imagery," Proc. SPIEVInt. Soc. Opt. Eng., vol. 5093, pp. 418-429, 2003.
55. C. Gonzalez, D. Mozos, J. Resano, and A. Plaza, "FPGA implementation of the N-FINDR algorithm for remotely sensed hyperspectral image analysis," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 2, pp. 374-388, Feb. 2012.
56. M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data," Proc. SPIEVInt. Soc. Opt. Eng., vol. 3753, pp. 266-275, 1999.
57. S. Torres-Rosario, "Iterative algorithms for abundance estimation on unmixing of hyperspectral imagery," M.S. thesis, Dept. Electr. Comput. Eng., Univ. Puerto Rico, San Juan, 2004.
58. C. Gonzalez, J. Resano, A. Plaza, and D. Mozos, "FPGA implementation of abundance estimation for spectral unmixing of hyperspectral data using the image space reconstruction algorithm," IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., vol. 5, no. 1, pp. 248-261, Feb. 2012.
59. C. Gonzalez, D. Mozos, J. Resano, and A. Plaza, "FPGA for computing the pixel purity index algorithm on hyperspectral images," Proc. Eng. Reconfigurable Syst. Algorithms Conf., 2010, pp. 125-131.
60. C. Gonzalez, J. Resano, D. Mozos, A. Plaza, and D. Valencia, "FPGA implementation of the pixel purity index algorithm for remotely sensed hyperspectral image analysis," EURASIP J. Adv. Signal Process., 2010, article 969806.
61. J. Boardman, "Automating spectral unmixing of AVIRIS data using convex geometry concepts," presented at the Summaries of Airborne Earth Science Workshop, 1993, JPL Publication 93-26, pp. 111-114.
62. D. Valencia, A. Plaza, M. A. Vega-Rodŕguez, and R. M. Pérez, "FPGA design and implementation of a fast pixel purity index algorithm for endmember extraction in hyperspectral imagery Proc. SPIEVInt. Soc. Opt. Eng., vol. 5995, 2005, DOI: 10.1117/12. 631270.
63. J. Morales, N. Medero, N. G. Santiago, and J. Sosa, "Hardware implementation of image space reconstruction algorithm using FPGAs," in Proc. 49th IEEE Int. Midwest Symp. Circuits Syst., 2006, vol. 1, pp. 433-436. (Pubitemid 47486872)
64. C.-I. Chang, W. Xiong, and C.-C. Wu, "Field-programmable gate array design of implementing simplex growing algorithm for hyperspectral endmember extraction," IEEE Trans. Geosci. Remote Sens., 2012, DOI: 10.1109/TGRS.2012.2207389.
65. C.-I. Chang, C. Wu,W. Liu, and Y. C. Ouyang, "A growing method for simplex-based endmember extraction algorithms," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2804-2819, Oct. 2006.
66. C.-I. Chang, C. C. Wu, C.-S. Lo, and M.-L. Chang, "Real-time simplex growing algorithms for hyperspectral endmember extraction," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 4, pp. 1834-1850, Apr. 2010.
67. W. Xiong, C.-C. Wu, C.-I. Chang, K. Kapalkis, and H. M. Chen, "Fast algorithms to implement N-FINDR for hyperspectral endmember extraction," IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., vol. 4, no. 3, pp. 545-564, Sep. 2011.
68. S. Lopez, P. Horstrand, G. M. Callico, J. F. Lopez, and R. Sarmiento, "A novel architecture for hyperspectral endmember extraction by means of the Modified Vertex Component Analysis (MVCA) algorithm," IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., vol. 5, no. 6, pp. 1837-1848, Dec. 2012.
69. S. Lopez, P. Horstrand, G. M. Callico, J. F. Lopez, and R. Sarmiento, "A Low-computational-complexity algorithm for hyperspectral endmember extraction: Modified vertex component analysis," IEEE Geosci. Remote Sens. Lett., vol. 9, no. 3, pp. 502-506, May 2012.
70. D. Hongtao and Q. Hairong, "An FPGA implementation of parallel ICA for dimensionality reduction in hyperspectral images," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2004, vol. 5, pp. 3257-3260.
71. M. Lennon, G. Mercier, M. C. Mouchot, and L. Hubert-Moy, "Independent component analysis as a tool for the dimensionality reduction and the representation of hyperspectral images," Proc. SPIEVInt. Soc. Opt. Eng., vol. 4541, pp. 2893-2895, 2001. (Pubitemid 34245331)
72. A. B. Lim, J. C. Rajapakse, and A. R. Omondi, "Comparative study of implementing ICNNs on FPGAs," in Proc. Int. Joint Conf. Neural Netw., 2001, vol. 1, pp. 177-182. (Pubitemid 32794825)
73. F. Sattar and C. Charayaphan, "Low-cost design and implementation of an ICA-based blind source separation algorithm," in Proc. Annu. IEEE Int. ASIC/SOC Conf., 2002, pp. 15-19.
74. A. Jacobs, C. Conger, and A. D. George, "Multiparadigm space processing for hyperspectral imaging," in Proc. IEEE Aerosp. Conf., 2008, vol. 1, pp. 8-11.
75. C. Chang, H. Ren, and S. Chiang, "Real-time processing algorithms for target detection and classification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 4, pp. 760-768, Apr. 2001. (Pubitemid 32432732)
76. C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Kluwer Academic/Plenum, 2003.
77. R. Nekovei and M. Ashtijou, "Reconfigurable acceleration for hyperspectral target detection," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2007, pp. 3229-3232.
78. Q. Du and H. Ren, "Real-time linear constrained discriminant analysis to hyperspectral imagery," Proc. SPIEVInt. Soc. Opt. Eng., vol. 4548, pp. 103-108, 2001. (Pubitemid 35173245)
79. S. Bernabe, S. Lopez, A. Plaza, R. Sarmiento, and P. G. Rodriguez, "FPGA design of an automatic target generation process for hyperspectral image analysis," in Proc. IEEE Int. Conf. Parallel Distrib. Syst., 2011, pp. 1010-1015.
80. K.-S. Park, S. H. Cho, S. Hong, and W.-D. Cho, "Real-time target detection architecture based on reduced complexity hyperspectral processing," EURASIP J. Adv. Signal Process., pp. 1-18, Sep. 2008, article 438051.
81. Q. Du and R. Nekovei, "Fast real-time onboard processing of hyperspectral imagery for detection and classification," J. Real-Time Image Process., vol. 4, no. 3, pp. 273-286, Aug. 2009.
82. Z. K. Baker, M. B. Gokhale, and J. L. Tripp, "Matched filter computation on FPGA, cell and GPU," in Proc. IEEE Symp. Field-Programmable Custom Comput. Mach., 2007, pp. 207-218.
83. Q. Du and J. E. Fowler, "Hyperspectral image compression using JPEG2000 and principal component analysis," IEEE Geosci. Remote Sens. Lett., vol. 4, no. 2, pp. 201-205, Apr. 2007. (Pubitemid 46645132)
84. N. R. Mat Noor, T. Vladimirova, and M. N. Sweeting, "High- performance lossless compression for hyperspectral satellite imagery," in Proc. UK Electron. Forum, 2010, pp. 78-83.
85. C. Egho, T. Vladimirova, and M. N. Sweeting, "Acceleration of Karhunen-Loève transform for system-on-chip platforms," in Proc. 7th NASA/ESA Conf. Adapt. Hardware Syst., 2012, pp. 272-279.
86. N. R. Mat Noor and T. Vladimirova, "Integer KLT design space exploration for hyperspectral satellite image compression," Lecture Notes in Computer Science, vol. 6935. Berlin, Germany: Springer-Verlag, 2011, pp. 661-668.
87. C. Egho and T. Vladimirova, "Hardware acceleration of Karhunen-Loeve transform for compression of hyperspectal satellite imagery," in Proc. Australian Space Sci. Conf., 2011, pp. 237-248.
88. P. Hao and Q. Shi, "Matrix factorizations for reversible integer mapping," IEEE Trans. Signal Process., vol. 49, no. 10, pp. 2314-2324, Oct. 2010.
89. M. Fleury, R. P. Self, and A. C. Downton, "Development of a fine-grained Karhunen-Loeve transform," J. Parallel Distrib. Comput., vol. 64, no. 4, pp. 520-535, Apr. 2004.
90. C. Egho and T. Vladimirova, "Hardware acceleration of the integer Karhunen-Loève transform algorithm for satellite image compression," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2012, pp. 4062-4065.
91. M. E. Winter, "A proof of the N-FINDR algorithm for the automated detection of endmembers in a hyperspectral image," Proc. SPIEVInt. Soc. Opt. Eng., vol. 5425, pp. 31-41, 2004.
92. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Trans. Geosci. Remote Sens., vol. 26, no. 1, pp. 65-74, Jan. 1988. (Pubitemid 18596008)
93. R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing. New York: Academic, 1997.
94. J. Tabero, H. Mecha, J. Septién, S. Román, and D. Mozos, "A vertex-list approach to 2D Hw multitasking management in RTR FPGAs," in Proc. DCIS Conf., 2003, pp. 545-550.
95. S. Román, J. Septién, H. Mecha, and D. Mozos, "Constant complexity management of 2D HW multitasking in run-time reconfigurable FPGAs," Lecture Notes in Computer Science, vol. 3985. Berlin, Germany: Springer-Verlag, 2006, pp. 187-192. (Pubitemid 44448053)
96. J. Resano, J. A. Clemente, C. González, D. Mozos, and F. Catthoor, "Efficiently scheduling runtime reconfigurations," ACM Trans. Design Autom. Electron. Syst., vol. 13, no. 4, pp. 58-69, Sep. 2008.
97. J. A. Clemente, C. González, J. Resano, and D. Mozos, "A task graph execution manager for reconfigurable multi-tasking systems," Microprocess. Microsyst., vol. 34, no. 2-4, pp. 73-83, Mar. 2010.
98. S. Lopez, G. M. Callico, A. Medina, J. F. Lopez, and R. Sarmiento, "High-level FPGA-based implementation of a hyperspectral Endmember Extraction Algorithm," in Proc. Workshop Hyperspectral Image Signal Process., Evol. Remote Sens., 2012, pp. 161-165.
99. T. Flatley, "SpaceCube: A family of reconfigurable hybrid on-board science data processors," presented at the NASA/ESA Conf. Adapt. Hardware Syst., Nuremberg, Germany, Jun. 25-28, 2012, Keynote Address I.