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Proceedings Paper

Analysis of signal-dependent sensor noise on JPEG 2000-compressed Sentinel-2 multi-spectral images
Author(s): M. Uss; B. Vozel; V. Lukin; K. Chehdi
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Paper Abstract

The processing chain of Sentinel-2 MultiSpectral Instrument (MSI) data involves filtering and compression stages that modify MSI sensor noise. As a result, noise in Sentinel-2 Level-1C data distributed to users becomes processed. We demonstrate that processed noise variance model is bivariate: noise variance depends on image intensity (caused by signal-dependency of photon counting detectors) and signal-to-noise ratio (SNR; caused by filtering/compression). To provide information on processed noise parameters, which is missing in Sentinel-2 metadata, we propose to use blind noise parameter estimation approach. Existing methods are restricted to univariate noise model. Therefore, we propose extension of existing vcNI+fBm blind noise parameter estimation method to multivariate noise model, mvcNI+fBm, and apply it to each band of Sentinel-2A data. Obtained results clearly demonstrate that noise variance is affected by filtering/compression for SNR less than about 15. Processed noise variance is reduced by a factor of 2 - 5 in homogeneous areas as compared to noise variance for high SNR values. Estimate of noise variance model parameters are provided for each Sentinel-2A band. Sentinel-2A MSI Level-1C noise models obtained in this paper could be useful for end users and researchers working in a variety of remote sensing applications.

Paper Details

Date Published: 4 October 2017
PDF: 12 pages
Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270Y (4 October 2017); doi: 10.1117/12.2278007
Show Author Affiliations
M. Uss, National Aerospace Univ. (Ukraine)
B. Vozel, Univ. de Rennes 1, IETR, CNRS (France)
V. Lukin, National Aerospace Univ. (Ukraine)
K. Chehdi, Univ. de Rennes 1, IETR, CNRS (France)

Published in SPIE Proceedings Vol. 10427:
Image and Signal Processing for Remote Sensing XXIII
Lorenzo Bruzzone, Editor(s)

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