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Ratio-Based Multitemporal SAR Images Denoising: RABASAR

Abstract : In this paper, 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. The proposed approach can be divided into three steps: 1) estimation of a “super-image” by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the “super-image”; 3) computation of the denoised image by re-multiplying the denoised ratio by the “super-image”. Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multi-temporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the “super-image” that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio, structure similarity index) as well as visually on simulated and SAR time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.
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Contributor : Loïc Denis Connect in order to contact the contributor
Submitted on : Monday, November 26, 2018 - 11:36:11 AM
Last modification on : Saturday, June 25, 2022 - 9:12:00 PM
Long-term archiving on: : Wednesday, February 27, 2019 - 1:22:37 PM


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Weiying Zhao, Charles-Alban Deledalle, Loïc Denis, Henri Maître, Jean-Marie Nicolas, et al.. Ratio-Based Multitemporal SAR Images Denoising: RABASAR. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, ⟨10.1109/TGRS.2018.2885683⟩. ⟨hal-01791355v2⟩



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