Ratio-based multi-temporal SAR images denoising

Weiying Zhao (1) , Loïc Denis (2) , Charles-Alban Deledalle (3) , Henri Maître (1) , Jean-Marie Nicolas (1) , Florence Tupin (1)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 LHC - Laboratoire Hubert Curien
3 IMB - Institut de Mathématiques de Bordeaux

—In this paper, we propose a generic multi-temporal SAR despeckling method to extend any single-image speckle reduction algorithm to multi-temporal stacks. Our method, RAtio-BAsed multi-temporal SAR despeckling (RABASAR), is based on ratios and fully exploits a " super-image " (i.e. temporal mean) in the process. The proposed approach can be divided into three steps: 1) calculation of the " super-image " through temporal averaging; 2) denoising the ratio images formed through dividing the noisy images by the " super-image " ; 3) computing denoised images by multiplying the denoised ratio images with the " super-image ". Thanks to the spatial stationarity improvement in the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising the original multi-temporal stack. The data volume to be processed is also reduced compared to other methods through the use of the " super-image ". The comparison with several state-of-the-art reference methods shows numerically (peak signal-noise-ratio, structure similarity index) and visually better results both on simulated and real SAR stacks. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods in order to exploit the temporal information of a time series.

Mots clés: Index Terms—Multi-temporal SAR series; speckle reduction; SAR; super- image; ratio image

Domaines: Traitement du signal et de l'image [eess.SP]

Weiying Zhao, Loïc Denis, Charles-Alban Deledalle, Henri Maître, Jean-Marie Nicolas, et al.. Ratio-based multi-temporal SAR images denoising. 2018. ⟨hal-01791355v1⟩