Skip to Main content Skip to Navigation
Conference papers

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

Abstract : The processing chain of Sentinel-2 Multi Spectral 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. © 2017 SPIE.
Complete list of metadatas

https://hal-univ-rennes1.archives-ouvertes.fr/hal-01713370
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, February 20, 2018 - 2:59:58 PM
Last modification on : Monday, October 5, 2020 - 9:50:26 AM

Identifiers

Citation

M. Uss, B. Vozel, V. Lukin, K. Chehdi. Analysis of signal-dependent sensor noise on JPEG 2000-compressed sentinel-2 multi-spectral images. Image and Signal Processing for Remote Sensing XXIII 2017, Sep 2017, Warsaw, Poland. pp. 2278007, ⟨10.1117/12.2278007⟩. ⟨hal-01713370⟩

Share

Metrics

Record views

144