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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 19, Iss. 14 — Jul. 4, 2011
  • pp: 13031–13046

Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing

Torbjørn Skauli  »View Author Affiliations

Optics Express, Vol. 19, Issue 14, pp. 13031-13046 (2011)

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Many types of hyperspectral image processing can benefit from knowledge of noise levels in the data, which can be derived from sensor physics. Surprisingly, such information is rarely provided or exploited. Usually, the image data are represented as radiance values, but this representation can lead to suboptimal results, for example in spectral difference metrics. Also, radiance data do not provide an appropriate baseline for calculation of image compression ratios. This paper defines two alternative representations of hyperspectral image data, aiming to make sensor noise accessible to image processing. A “corrected raw data” representation is proportional to the photoelectron count and can be processed like radiance data, while also offering simpler estimation of noise and somewhat more compact storage. A variance-stabilized representation is obtained by square-root transformation of the photodetector signal to make the noise signal-independent and constant across all bands while also reducing data volume by almost a factor 2. Then the data size is comparable to the fundamental information capacity of the sensor, giving a more appropriate measure of uncompressed data size. It is noted that the variance-stabilized representation has parallels in other fields of imaging. The alternative data representations provide an opportunity to reformulate hyperspectral processing algorithms to take actual sensor noise into account.

© 2011 OSA

OCIS Codes
(110.4280) Imaging systems : Noise in imaging systems
(100.4145) Image processing : Motion, hyperspectral image processing
(110.4234) Imaging systems : Multispectral and hyperspectral imaging

ToC Category:
Image Processing

Original Manuscript: January 19, 2011
Revised Manuscript: June 2, 2011
Manuscript Accepted: June 8, 2011
Published: June 22, 2011

Torbjørn Skauli, "Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing," Opt. Express 19, 13031-13046 (2011)

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  1. B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Rem. Sens. 45(5), 1408–1421 (2007). [CrossRef]
  2. Q. Du and J. E. Fowler, “Hyperspectral image compression using JPEG2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4(2), 201–205 (2007). [CrossRef]
  3. J. Wang and C. I. Chang, “Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis,” IEEE Trans. Geosci. Rem. Sens. 44(6), 1586–1600 (2006). [CrossRef]
  4. B. Penna, T. Tillo, E. Magli, and G. Olmo, “Progressive 3-D coding of hyperspectral images based on JPEG 2000,” IEEE Geosci. Remote Sens. Lett. 3(1), 125–129 (2006). [CrossRef]
  5. J. Mielikainen and P. Toivanen, “Clustered DPCM for the lossless compression of hyperspectral images,” IEEE Trans. Geosci. Rem. Sens. 41(12), 2943–2946 (2003). [CrossRef]
  6. B. Aiazzi, L. Alparone, and S. Baronti, “Near-lossless compression of 3-D optical data,” IEEE Trans. Geosci. Rem. Sens. 39(11), 2547–2557 (2001). [CrossRef]
  7. M. J. Ryan and J. F. Arnold, “Lossy compression of hyperspectral data using vector quantization,” Remote Sens. Environ. 61(3), 419–436 (1997). [CrossRef]
  8. M. J. Ryan and J. F. Arnold, “The lossless compression of AVIRIS images by vector quantization,” IEEE Trans. Geosci. Rem. Sens. 35(3), 546–550 (1997). [CrossRef]
  9. S. E. Qian, A. B. Hollinger, D. Williams, and D. Manak, “Fast three-dimensional data compression of hyperspectral imagery using vector quantization with spectral-feature-based binary coding,” Opt. Eng. 35(11), 3242–3249 (1996). [CrossRef]
  10. R. E. Roger and M. C. Cavenor, “Lossless compression of AVIRIS images,” IEEE Trans. Image Process. 5(5), 713–719 (1996). [CrossRef] [PubMed]
  11. G. P. Abouselman, M. W. Marcellin, and B. R. Hunt, “Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT,” IEEE Trans. Geosci. Rem. Sens. 33(1), 26–34 (1995). [CrossRef]
  12. Recent papers on hyperspectral compression were retrieved from the ISI Web of science database in January 2011, but not listed here for space reasons. References are available from the author.
  13. J. W. Boardman, “Using dark current data to estimate AVIRIS noise covariance and improve spectral processing” in Summaries of the Fifth Annual JPL Airborne Geoscience Workshop, Pasadena, CA 1995.
  14. T. Skauli, T. V. Haavardsholm, I. Kåsen, G. Arisholm, A. Kavara, T. O. Opsahl, and A. Skaugen, “An airborne real-time hyperspectral target detection system,” Proc. SPIE 7695, 76950A, 76950A-6 (2010). [CrossRef]
  15. A. S. Norsk Elektro Optikk, http://www.neo.no/products/hyperspectral.html .
  16. T. Skauli, “Sensor-informed representation of hyperspectral images,” Proc. SPIE 7334, 733418, 733418-8 (2009). [CrossRef]
  17. E. L. Dereniak and G. D. Boreman, Infrared Detectors and Systems (Wiley, New York 1996).
  18. P. C. D. Hobbs, Building Electro-Optical Systems: Making It All Work, 2nd ed. (Wiley, 2009).
  19. Spectral Imaging, Ltd., http://www.specim.fi .
  20. R. O. Green, M. L. Eastwood, C. M. Sarture, T. G. Chrien, M. Aronsson, B. J. Chippendale, J. A. Faust, B. E. Pavri, C. J. Chovit, M. Solis, M. R. Olah, and O. Williams, “Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ. 65(3), 227–248 (1998). [CrossRef]
  21. J. Hynecek, “Impactron - A new solid state image intensifier,” IEEE Trans. Electron. Dev. 48(10), 2238–2241 (2001). [CrossRef]
  22. B. Fowler, C. Liu, S. Mims, J. Balicki, W. Li, H. Do, J. Appelbaum, and P. Vu, “A 5.5Mpixel 100 frames/sec wide dynamic range low noise CMOS image sensor for scientific applications,” Proc. SPIE 7536, 753607, 753607-12 (2010). [CrossRef]
  23. J. D. Beck, C.-F. Wan, M. A. Kinch, J. E. Robinson, P. Mitra, R. E. Scritchfield, F. Ma, and J. C. Campbell, “The HgCdTe electron avalanche photodiode,” J. Electron. Mater. 35(6), 1166–1173 (2006). [CrossRef]
  24. A. Martinez, “Capacity bounds for the Einstein radiation channel,” Proc. Int. Symp. Inf. Theory, ISIT 2006, 9–14 July 2006, Seattle (USA), pp. 366–370 (2006).
  25. F. J. Anscombe, “The transformation of Poisson, binomial and negative-binomial data,” Biometrika 35, 246–254 (1948) (Curiously, Anscombe credits the “Anscombe transform” to A. H. L. Johnson.).
  26. J. A. Rice, Mathematical Statistics and Data Analysis, (Duxbury press, 1995) p. 321.
  27. R. L. White and J. W. Percival, “Compression and progressive transmission of astronomical images,” Proc. SPIE 2199, 703–713 (1994). [CrossRef]
  28. M. A. Nieto-Santisteban, D. J. Fixsen, J. D. Offenberg, R. J. Hanisch, and H. S. Stockman, “Data compression for NGST” in Proceedings of Astronomical data analysis software and systems VIII, ASP Conference series 172, 137–140 (1999).
  29. R. A. Gowen and A. Smith, “Square root data compression,” Rev. Sci. Instrum. 74(8), 3853–3861 (2003). [CrossRef]
  30. R. L. Seaman, R. L. White, and W. D. Pence, “Optimal DN encoding for CCD detectors” in Proceedings of Astronomical Data Analysis Software and Systems XVII, ASP Conference Series, Vol. 411, 101 (2009).
  31. G. M. Bernstein, C. Bebek, J. Rhodes, C. Stoughton, R. A. Vanderveld, and P. Yeh, “Noise and bias in square-root compression schemes,” Proc. Astron. Soc. Pacific 122(889), 336–346 (2010). [CrossRef]
  32. F. Murtagh, J.-L. Starck, and A. Bijaoui, “Image restoration with noise suppression using a multiresolution support,” Astron. Astrophys. Suppl. Ser. 112, 179–189 (1995).
  33. J.-L. Starck and F. Murtagh, “Astronomical image and signal processing: looking at noise, information, and scale,” IEEE Signal Process. Mag. 18(2), 30–40 (2001). [CrossRef]
  34. B. Zhang, M. J. Fadili, J.-L. Starck, and J.-C. Olivo-Marin, “Multiscale variance-stabilizing transform for mixed-poisson-gaussian processes and its Applications in Bioimaging” in Proceedings of IEEE International conference on image processing (Institute of Electrical and Electronics Engineers, New York, 2007) pp. VI233–VI236.
  35. C. Poynton, A Technical Introduction to Digital Video (Wiley, 1996), Chap. 6.
  36. M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the internet - sRGB”, http://www.w3.org/Graphics/Color/sRGB (1996).

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