RABASAR: A fast ratio based multi-temporal SAR despeckling

This work has been done in collaboration with Loïc Denis, Charles-Alban Deledalle, Henri Maître, Jean-Marie Nicolas and Florence Tupin.

The principle of the proposed method

The proposed approach can be divided into three steps:

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 multitemporal stack. The data volume to be processed is also reduced compared to other methods through the use of the “super-image”.

Flowchart Figure 1. Multi-temporal SAR image denoising framework

The interest of ratio image

In all situations, ratio image with fewer structures and variations than an original SAR image
→ Easier to denoise !

Experimental results

NoisySuperImg Figure 2. Noisy image and MuLog-BM3D denoised super-image.


Figure 3. RABASAR denoised image


[1] Deledalle, C.A., Denis, L., Tabti, S. and Tupin, F., 2017. MuLoG, or How to apply Gaussian denoisers to multi-channel SAR speckle reduction?. IEEE Transactions on Image Processing, 26(9), pp.4389-4403.

[2] Zhao, W., Deledalle, C.A., Denis, L., Maître, H., Nicolas, J.M. and Tupin, F., 2018, July. RABASAR: A FAST RATIO BASED MULTI-TEMPORAL SAR DESPECKLING. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018.

[3] Zhao, W., Denis, L., Deledalle, C.A., Maître, H., Nicolas, J-M. and Tupin, F. Ratio-Based Multitemporal SAR Images Denoising: RABASAR. IEEE Transactions on Geoscience and Remote Sensing. 2019.

Code links

MuLog-BM3D: https://www.math.u-bordeaux.fr/~cdeledal/mulog.php

RABASAR https://bitbucket.org/cdeledalle/rabasar