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Illustration de l'actualité : PhD defense Weiying Zhao : Multitemporal SAR images denoising and change detection–Applications to Sentinel-1 data

PhD defense Weiying Zhao : Multitemporal SAR images denoising and change detection–Applications to Sentinel-1 data

lundi
21
janvier
2019

PhD Comics : I'm defending my thesis, Mom !

Jury

  • Mme Benédicte Fruneau, Université-Paris-Est-Marne-la-Vallée, Rapporteure
  • M. Jordi Inglada, CESBIO (CNES/CNRS/UPS/IRD), Rapporteur
  • M. Rene Garello, IMT Atlantique Brest, Examinateur                      
  • Mme Hong Sun, Université de Wuhan, Chine Examinateur
  • M. Frederic Schmidt, Université Paris-Saclay, Examinateur
  • Mme Florence Tupin, Télécom ParisTech, Directrice de thèse
  • M. Henri Maître, Télécom ParisTech, Co-directeur de thèse

Abstract

The inherent speckle which is attached to any coherent imaging system affects the analysis and interpretation of synthetic aperture radar (SAR) images. To take advantage of well-registered multi-temporal SAR images, we improve the adaptive nonlocal temporal filter with state-of-the-art adaptive denoising methods and propose a patch based adaptive temporal filter. To address the bias problem of the denoising results, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well-preserved thanks to the multi-temporal mean. Without the reference image, we propose to use a patch-based auto-covariance residual evaluation method to examine the residual image and look for possible remaining structural contents.