4.7 Article

Nonlocal Multiscale Single Image Statistics From Sentinel-1 SAR Data for High Resolution Bitemporal Forest Wind Damage Detection

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2022.3169473

关键词

Synthetic aperture radar (SAR) data; vegetation and land surface

资金

  1. Ministry of Agriculture and Forestry in the Project Tuulituho-Haukka

向作者/读者索取更多资源

This study presents a new nonlocal averaging approach (STAl'SAR) to reduce speckle in high-resolution SAR images and improve the resolution of statistical parameters. The approach analyzes the similarity of SAR pixels based on statistical data and applies K-means clustering for image segmentation. Nonlocal averaging filtering is applied to both pixel backscattering values and statistical parameters.
Change detection of synthetic aperture radar (SAR) data is a challenge for high-resolution applications. This study presents a new nonlocal averaging approach (STAl'SAR) to reduce the speckle of single Sentinel-1 SAR images and statistical parameters derived from the image. The similarity of SAR pixels is based on the statistics of 3 x 3 window as represented by the mean, standard deviation, median, minimum, and maximum. K-means clustering is used to divide the SAR image in 30 similarity clusters. The nonlocal averaging is carried out within each cluster separately in magnitude order of the 3 x 3 window averages. The nonlocal filtering is applicable not only to the original pixel backscattering values but also to statistical parameters, such as standard deviation. The statistical parameters to be filtered can represent any window size, according to the need of the application. The nonlocally averaged standard deviation derived in two spatial resolutions, 3 x 3 and 7 x 7 windows, are demonstrated here for improving the resolution in which the forest damages can be detected using the VH polarized backscattering spatial variation change.

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