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Proceedings Paper

Bias estimation for the Landsat 8 operational land imager
Author(s): K. Vanderwerff; R. Morfitt
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Paper Abstract

The Operational Land Imager (OLI) is a pushbroom sensor that will be a part of the Landsat Data Continuity Mission (LDCM). This instrument is the latest in the line of Landsat imagers, and will continue to expand the archive of calibrated earth imagery. An important step in producing a calibrated image from instrument data is accurately accounting for the bias of the imaging detectors. Bias variability is one factor that contributes to error in bias estimation for OLI. Typically, the bias is simply estimated by averaging dark data on a per-detector basis. However, data acquired during OLI pre-launch testing exhibited bias variation that correlated well with the variation in concurrently collected data from a special set of detectors on the focal plane. These detectors are sensitive to certain electronic effects but not directly to incoming electromagnetic radiation. A method of using data from these special detectors to estimate the bias of the imaging detectors was developed, but found not to be beneficial at typical radiance levels as the detectors respond slightly when the focal plane is illuminated. In addition to bias variability, a systematic bias error is introduced by the truncation performed by the spacecraft of the 14-bit instrument data to 12-bit integers. This systematic error can be estimated and removed on average, but the per pixel quantization error remains. This paper describes the variability of the bias, the effectiveness of a new approach to estimate and compensate for it, as well as the errors due to truncation and how they are reduced.

Paper Details

Date Published: 13 September 2011
PDF: 12 pages
Proc. SPIE 8153, Earth Observing Systems XVI, 81530H (13 September 2011); doi: 10.1117/12.896221
Show Author Affiliations
K. Vanderwerff, SGT Inc. (United States)
R. Morfitt, SGT Inc. (United States)


Published in SPIE Proceedings Vol. 8153:
Earth Observing Systems XVI
James J. Butler; Xiaoxiong Xiong; Xingfa Gu, Editor(s)

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