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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)
http://dx.doi.org/10.1364/OE.19.013031


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Abstract

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

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

Citation
Torbjørn Skauli, "Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing," Opt. Express 19, 13031-13046 (2011)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-14-13031


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