利用rsamnyi熵量化SAR图像的异质性

IF 4.4
Janeth Alpala;Abraão D. C. Nascimento;Alejandro C. Frery
{"title":"利用rsamnyi熵量化SAR图像的异质性","authors":"Janeth Alpala;Abraão D. C. Nascimento;Alejandro C. Frery","doi":"10.1109/LGRS.2025.3581855","DOIUrl":null,"url":null,"abstract":"Quantifying heterogeneity in synthetic aperture radar (SAR) data is critical for accurate geophysical interpretation and remote sensing applications. We propose a test statistic based on a nonparametric estimation of Rényi entropy to characterize return heterogeneity from SAR intensity data. The statistic is refined using bootstrap to improve its stability, size, and power. This approach enhances heterogeneity quantification by capturing scale-dependent variations and addressing data-driven uncertainty. Experimental results establish the robustness of the proposed method in distinguishing heterogeneity patterns.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4000,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantifying Heterogeneity in SAR Imagery With the Rényi Entropy\",\"authors\":\"Janeth Alpala;Abraão D. C. Nascimento;Alejandro C. Frery\",\"doi\":\"10.1109/LGRS.2025.3581855\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantifying heterogeneity in synthetic aperture radar (SAR) data is critical for accurate geophysical interpretation and remote sensing applications. We propose a test statistic based on a nonparametric estimation of Rényi entropy to characterize return heterogeneity from SAR intensity data. The statistic is refined using bootstrap to improve its stability, size, and power. This approach enhances heterogeneity quantification by capturing scale-dependent variations and addressing data-driven uncertainty. Experimental results establish the robustness of the proposed method in distinguishing heterogeneity patterns.\",\"PeriodicalId\":91017,\"journal\":{\"name\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"volume\":\"22 \",\"pages\":\"1-5\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11045765/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11045765/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

量化合成孔径雷达(SAR)数据的非均质性对于精确的地球物理解释和遥感应用至关重要。我们提出了一个基于rsamnyi熵非参数估计的检验统计量来表征SAR强度数据的回归异质性。统计数据使用bootstrap进行细化,以提高其稳定性、大小和功率。这种方法通过捕获尺度相关的变化和处理数据驱动的不确定性来增强异质性量化。实验结果证明了该方法在识别异质性模式方面的稳健性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying Heterogeneity in SAR Imagery With the Rényi Entropy
Quantifying heterogeneity in synthetic aperture radar (SAR) data is critical for accurate geophysical interpretation and remote sensing applications. We propose a test statistic based on a nonparametric estimation of Rényi entropy to characterize return heterogeneity from SAR intensity data. The statistic is refined using bootstrap to improve its stability, size, and power. This approach enhances heterogeneity quantification by capturing scale-dependent variations and addressing data-driven uncertainty. Experimental results establish the robustness of the proposed method in distinguishing heterogeneity patterns.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信