高分辨率SAR子孔径图像相干性分析 及其地物分类应用

doi:10.7523/j.ucas.2021.0052

中国科学院大学学报 ›› 2022, Vol. 39 ›› Issue (6): 764-775.DOI: 10.7523/j.ucas.2021.0052

• 电子信息与计算机科学 • 上一篇下一篇

高分辨率SAR子孔径图像相干性分析及其地物分类应用

邢文继^1,3, 金燕², 仇晓兰^1,2, 丁赤飚^1,2,3, 周晓¹

1. 中国科学院空天信息创新研究院中国科学院空间信息处理与应用系统技术重点实验室, 北京 100190;
2. 苏州空天信息研究院, 江苏苏州 215000;
3. 中国科学院大学, 北京 100190

收稿日期:2020-05-20 修回日期:2020-11-16 发布日期:2022-11-11
通讯作者: 金燕,E-mail:yjin@mail.ie.ac.cn
基金资助:
国家自然科学基金(61991421)和国家重点研发计划(2018YFA0701903)资助

Coherence analysis of high resolution SAR sub-aperture image and its application in ground feature classification

XING Wenji^1,3, JIN Yan², QIU Xiaolan^1,2, DING Chibiao^1,2,3, ZHOU Xiao¹

1. CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China;
2. Suzhou Aerospace Information Research Institute, Suzhou 215121, Jiangsu, China;
3. University of Chinese Academy of Sciences, Beijing 100190, China

Received:2020-05-20 Revised:2020-11-16 Published:2022-11-11

摘要/Abstract

摘要： 针对相干性这一合成孔径雷达(SAR)图像分析常用的特征量进行分析，首先从理论上分析高分辨率SAR图像中人造目标和自然地物等典型目标的子孔径、子频带以及不同子孔径重轨干涉图像间的相干系数，然后利用高分辨率星载SAR实际数据开展上述相干系数的计算，验证分析的正确性。随后根据不同地物在不同维度相干系数上体现的不同特点，进行非监督地物分类，并给出不同类别所表征的地物特点。分析结果可为高分辨率SAR数据的优化应用提供支撑，并可加深对SAR不同地物目标特性的理解。

关键词: 高分辨率SAR, 相干性, 子孔径, 成像处理带宽, 图像分类

Abstract: With the continuous improvement of synthetic aperture radar (SAR) resolution, the transmitted signal bandwidth and synthetic aperture are continuously increasing, which provide more options for subsequent applications. How to develop the potential of high resolution SAR with large synthetic aperture and large signal bandwidth in the application of ground feature classification and interference is worth studying. Coherence, the feature most commonly used in SAR image analysis, is analyzed in this paper. Firstly, the coherence coefficients between the sub-apertures, sub-bands, and repeat-pass interferometric sub-apertures of typical targets, such as man-made targets and natural features, are analyzed theoretically. Then, the above coherence coefficients are calculated using the real data of high-resolution spaceborne SAR to verify the correctness of the analysis. And then, unsupervised feature classifications are performed according to different features of different ground objects in different coherence coefficients, and the features represented by different categories were given. The analysis results in this paper provide support for the optimization application of high-resolution SAR data, and deepen the understanding of the characteristics of different SAR targets.

Key words: high resolution SAR, coherence, sub-apterture, imaging processing bandwidth, image classification

中图分类号:

TN957.52

邢文继, 金燕, 仇晓兰, 丁赤飚, 周晓. 高分辨率SAR子孔径图像相干性分析及其地物分类应用[J]. 中国科学院大学学报, 2022, 39(6): 764-775.

XING Wenji, JIN Yan, QIU Xiaolan, DING Chibiao, ZHOU Xiao. Coherence analysis of high resolution SAR sub-aperture image and its application in ground feature classification[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(6): 764-775.

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高分辨率SAR子孔径图像相干性分析及其地物分类应用

Coherence analysis of high resolution SAR sub-aperture image and its application in ground feature classification

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高分辨率SAR子孔径图像相干性分析 及其地物分类应用

Coherence analysis of high resolution SAR sub-aperture image and its application in ground feature classification

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高分辨率SAR子孔径图像相干性分析及其地物分类应用