压缩感知在城区高分辨率SAR层析成像中的应用

廖明生; 魏恋欢; 汪紫芸; TimoBalz; 张路

doi:10.12000/JR15031

压缩感知在城区高分辨率SAR层析成像中的应用

doi: 10.12000/JR15031

(武汉大学测绘遥感信息工程国家重点实验室武汉 430079)

基金项目:

国家自然科学基金(61331016, 41174120)和高等学校博士学科点专项科研基金(20110141110057)资助课题

详细信息

作者简介:
廖明生(1962–)，男，武汉大学教授，博士生导师，珞珈杰出学者。主要从事航空航天遥感影像信息处理的理论与方法的研究。E-mail:liao@whu.edu.cn魏恋欢(1986–)，女，武汉大学与慕尼黑工业大学联合培养博士研究生。主要从事合成孔径雷达干涉测量及层析成像研究。E-mail:lianhuan@whu.edu.cn汪紫芸(1988–)，女，武汉大学博士研究生，主要研究方向为合成孔径雷达遥感。E-mail:ziyunwang@whu.edu.cn

通讯作者:
廖明生liao@whu.edu.cn

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出版历程
- 收稿日期: 2015-03-11
- 修回日期: 2015-04-09
- 网络出版日期: 2015-04-28

Compressive Sensing in High-resolution 3D SAR Tomography of Urban Scenarios

(State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

摘要

摘要: 在建筑密集的城区复杂场景中,高分辨率SAR影像中存在严重的叠掩效应,影像解译的难度加大.SAR层析成像可以分离单个分辨单元内混叠的散射体目标,并且获取各个散射体的3维位置和后向散射强度.该文首先论述了3维SAR层析成像的基本原理,针对传统谱估计法获得的高程向分辨率较低的问题,综述了压缩感知方法在城区3维SAR层析成像中的应用,以基追踪和双步迭代收缩阈值法为例,开展了TerraSAR-X聚束模式数据实验,并与传统的奇异值阈值法进行了对比分析.研究结果表明压缩感知方法的高程向超分辨率、旁瓣抑制优势明显,在城区SAR层析成像中具有广阔的应用前景.
- SAR层析成像 /
- 压缩感知(CS) /
- 稀疏重建 /
- 基追踪 /
- 双步迭代收缩阈值 /
- 超分辨率
Abstract: In modern high resolution SAR data, due to the intrinsic side-looking geometry of SAR sensors, layover and foreshortening issues inevitably arise, especially in dense urban areas. SAR tomography provides a new way of overcoming these problems by exploiting the back-scattering property for each pixel. However, traditional non-parametric spectral estimators, e.g. Truncated Singular Value Decomposition (TSVD), are limited by their poor elevation resolution, which is not comparable to the azimuth and slant-range resolution. In this paper, the Compressive Sensing (CS) approach using Basis Pursuit (BP) and TWo-step Iterative Shrinkage/Thresholding (TWIST) are introduced. Experimental studies with real spotlight-mode TerraSAR-X dataset are carried out using both BP and TWIST, to demonstrate the merits of compressive sensing approaches in terms of robustness, computational efficiency, and super-resolution capability.
- SAR tomography /
- Compressive Sensing (CS) /
- Sparse reconstruction /
- Basis pursuit /
- TWo-step Iterative Shrinkage/Thresholding (TWIST) /
- Super resolution

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