%0 Conference Proceedings %T Analysis of signal-dependent sensor noise on JPEG 2000-compressed sentinel-2 multi-spectral images %+ Kharkov National University %+ Institut d'Électronique et des Technologies du numéRique (IETR) %A Uss, M. %A Vozel, B. %A Lukin, V. %A Chehdi, K. %< avec comité de lecture %B Image and Signal Processing for Remote Sensing XXIII 2017 %C Warsaw, Poland %Y Bovolo F.Bruzzone L. %I SPIE %V 10427 %P 2278007 %8 2017-09-11 %D 2017 %R 10.1117/12.2278007 %K Blind noise parameter estimation %K Image compression %K Image denoising %K Multi spectral instrument %K Multivariate noise variance model %K Processed noise %K Remote sensing %K Sentinel-2 %K Signal-dependency %Z Computer Science [cs]/Networking and Internet Architecture [cs.NI] %Z Engineering Sciences [physics]/ElectronicsConference papers %X 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. %G English %L hal-01713370 %U https://univ-rennes.hal.science/hal-01713370 %~ UNIV-NANTES %~ UNIV-RENNES1 %~ CNRS %~ INSA-RENNES %~ IETR %~ STATS-UR1 %~ CENTRALESUPELEC %~ UR1-HAL %~ UR1-MATH-STIC %~ UR1-UFR-ISTIC %~ IETR-SHINE %~ IETR-OS %~ TEST-UNIV-RENNES %~ TEST-UR-CSS %~ UNIV-RENNES %~ INSA-GROUPE %~ TEST-HALCNRS %~ UR1-MATH-NUM %~ IETR-MULTIP %~ NANTES-UNIVERSITE %~ UNIV-NANTES-AV2022