$M$ -NL: Robust NL-Means Approach for PolSAR Images Denoising

Gordana Draskovic (1) , Frédéric Pascal (1) , Florence Tupin (2, 3, 4)

1 L2S - Laboratoire des signaux et systèmes
2 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse
3 IDS - Département Images, Données, Signal
4 IP Paris - Institut Polytechnique de Paris

This paper proposes a new method for polarimetric synthetic aperture radar (PolSAR) denoising. More precisely, it seeks to address a new statistical approach for weights computation in non-local (NL) approaches. The aim is to present a simple criterion using M-estimators and to detect similar pixels in an image. A binary hypothesis test is used to select similar pixels which will be used for covariance matrix estimation together with associated weights. The method is then compared to an advanced state of the art PolSAR denoising method, NL-SAR method [1]. The filter performances are measured by a set of different indicators, including relative errors on incoherent target decomposition parameters, coherences, polarimetric signatures, and edge preservation on a set of simulated PolSAR images, as in [2]. Finally, results for RADARSAT-2 PolSAR data are presented.

Keywords: Index Terms-PolSAR; NL means; M -estimators; Wishart distribution; Detection

Domains: Computer Science [cs]; Signal and Image Processing; Statistics [stat]; Other Statistics [stat.ML]; Statistics [stat]; Applications [stat.AP]

Gordana Draskovic, Frédéric Pascal, Florence Tupin. $M$ -NL: Robust NL-Means Approach for PolSAR Images Denoising. IEEE Geoscience and Remote Sensing Letters, 2019, 16 (6), pp.997-1001. ⟨10.1109/LGRS.2018.2889275⟩. ⟨hal-02186186⟩