Analysis of signal-dependent sensor noise on JPEG 2000-compressed Sentinel-2 multi-spectral images

M. Uss; B. Vozel; V. Lukin; K. Chehdi

doi:10.1117/12.2278007

4 October 2017 Analysis of signal-dependent sensor noise on JPEG 2000-compressed Sentinel-2 multi-spectral images

M. Uss, B. Vozel, V. Lukin, K. Chehdi

Proceedings Volume 10427, Image and Signal Processing for Remote Sensing XXIII; 104270Y (2017) https://doi.org/10.1117/12.2278007
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

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.

Citation Download Citation

M. Uss, B. Vozel, V. Lukin, and K. Chehdi "Analysis of signal-dependent sensor noise on JPEG 2000-compressed Sentinel-2 multi-spectral images", Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270Y (4 October 2017); https://doi.org/10.1117/12.2278007

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KEYWORDS

Signal to noise ratio

Image compression

Sensors

Remote sensing

Hyperspectral imaging

Image analysis

Interference (communication)

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