Hyperspectral images are customarily stored and transferred as radiance values. Image analysis may benefit from additional sensor-related information such as signal-dependent noise levels. This paper discusses representations of hyperspectral image data in forms which are intermediate between raw data and radiance data. The intermediate-form data can be processed directly, or they can be readily converted into radiance values and estimates of signal-dependent noise. The metadata needed for this data transformation constitutes an informative first-order description of the sensor, as an added benefit for the data user. One of the proposed data formats has already been adopted in commercial hyperspectral sensors. The proposed representations can be stored in a more compact data format than radiance values without loss of information, under reasonable assumptions about the sensor properties. In particular, it will be shown that a square-root transformation of the data leads to a representation which approaches the information-theoretic lower limit for storing light samples. The use of noise estimates derived from sensor physics is likely to be useful in hyperspectral image processing and image compression.
[1]
Yuliya Tarabalka,et al.
Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing
,
2009,
Journal of Real-Time Image Processing.
[2]
Alfonso Martinez,et al.
Capacity Bounds for the Einstein Radiation Channel
,
2006,
2006 IEEE International Symposium on Information Theory.
[3]
Richard E. Ladner,et al.
Near-Lossless Compression of Hyperspectral Images
,
2006,
2006 International Conference on Image Processing.
[4]
J. Rice.
Mathematical Statistics and Data Analysis
,
1988
.
[5]
Amos Lapidoth,et al.
On the Capacity of the Discrete-Time Poisson Channel
,
2009,
IEEE Transactions on Information Theory.
[6]
Yuliya Tarabalka,et al.
Status of the Norwegian hyperspectral technology demonstrator
,
2008
.
[7]
Matthew Anderson,et al.
Proposal for a Standard Default Color Space for the Internet - sRGB
,
1996,
CIC.
[8]
E. Dereniak,et al.
Infrared Detectors and Systems
,
1996
.