Speckle Noise Reduction In Sar Images Using Information Theory

D. Chan, J. Gambini, A. Frery
{"title":"Speckle Noise Reduction In Sar Images Using Information Theory","authors":"D. Chan, J. Gambini, A. Frery","doi":"10.1109/lagirs48042.2020.9165582","DOIUrl":null,"url":null,"abstract":"In this work, a new nonlocal means filter for single-look speckled data using the Shannon and Renyi entropies is proposed. The measure of similarity between a central window and patches of the image is based on a statistical test for comparing if two samples have the same entropy and hence have the same distribution.The results are encouraging, as the filtered image has better signal-to-noise ratio, it preserves the mean, and the edges are not severely blurred.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/lagirs48042.2020.9165582","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In this work, a new nonlocal means filter for single-look speckled data using the Shannon and Renyi entropies is proposed. The measure of similarity between a central window and patches of the image is based on a statistical test for comparing if two samples have the same entropy and hence have the same distribution.The results are encouraging, as the filtered image has better signal-to-noise ratio, it preserves the mean, and the edges are not severely blurred.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于信息理论的Sar图像散斑降噪
本文提出了一种基于Shannon熵和Renyi熵的非局部均值滤波方法。测量中心窗口和图像斑块之间的相似性是基于比较两个样本是否具有相同的熵从而具有相同的分布的统计测试。结果令人鼓舞,滤波后的图像具有较好的信噪比,保持了均值,边缘没有严重模糊。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Deforestation Polygon Assessment Tool: Providing Comprehensive Information On Deforestation In The Brazilian Cerrado Biome Assessment of rainfall influence on sentinel-1 time series on amazonian tropical forests aiming deforestation detection improvement Spatial Association To Characterize The Climate Teleconnection Patterns In Ecuador Based On Satellite Precipitation Estimates Subsidence in Maceio, Brazil, Characterized by Dinsar and Inverse Modeling Preliminary Analysis For Automatic Tidal Inlets Mapping Using Google Earth Engine
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1