4.7 Article

Ratio-Based Multitemporal SAR Images Denoising: RABASAR

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2018.2885683

关键词

Multitemporal synthetic aperture radar (SAR) series; ratio image; speckle reduction; superimage

资金

  1. CNES (French Space Agency) [DAJ/AR/IB-2016-10117102]
  2. China Scholarship Council

向作者/读者索取更多资源

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 multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a superimage by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the superimage; and 3) computation of the denoised image by remultiplying the denoised ratio by the superimage. Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a specklereduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the superimage that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据