基于块压缩感知的SAR层析成像方法

王爱春; 向茂生

doi:10.12000/JR16006

基于块压缩感知的SAR层析成像方法

DOI: 10.12000/JR16006

王爱春^,,
向茂生

1.
(中国科学院电子学研究所微波成像技术国家级重点实验室北京 100190)
2.
(中国科学院大学北京 100049)
3.
(中国资源卫星应用中心北京 100094)

基金项目:

国家发改委卫星及应用产业发展专项项目发改委高技【2012】2083号

详细信息

作者简介:
王爱春(1981-),男,内蒙古和林格尔县人,中国资源卫星应用中心工程师,中国科学院电子学研究所在读博士生,研究方向为多基线干涉SAR处理方法及应用。E-mail:wangaichun@cresda.com向茂生(1964-),男,中国科学院电子学研究所研究员,博士生导师,研究方向为干涉合成孔径雷达系统技术和方法。E-mail:xms@mail.ie.ac.cn

通讯作者:
王爱春wangaichun@cresda.com

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- 被引次数: 0
出版历程
- 收稿日期: 2016-01-11
- 修回日期: 2016-01-27
- 网络出版日期: 2016-02-28

SAR Tomography Based on Block Compressive Sensing

Wang Aichun^,,
Xiang Maosheng

1.
(National Key Laboratory of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
2.
(University of Chinese Academy of Sciences, Beijing 100049, China)
3.
(China Center for Resources Satellite Data and Application, Beijing 100094, China)

Funds:

National Development and Reform Commission Satellite and Application Development Projects【2012】2083

摘要

摘要: 基于压缩感知(Compressive Sensing, CS)的SAR层析成像方法(SAR Tomography, TomoSAR),虽然实现了对目标的3维重构,但对于具有结构特性的目标其重构性能较差。针对这一问题,该文提出了采用块压缩感知(Block Compressive Sensing, BCS)算法,该方法首先在CS方法基础上将具有结构特性的目标信号重构问题转化为BCS问题,然后根据目标结构特性与雷达参数的关系确定块的大小,最后对目标进行块稀疏的l1/l2范数最优化求解。相比基于CS的SAR层析成像方法,该方法更好地利用了目标的稀疏特性和结构特性,其重构精度更高、性能更优。仿真数据和Radarsat-2星载SAR实测数据的试验结果验证了该方法的有效性。
- SAR层析成像技术 /
- 压缩感知 /
- 块压缩感知 /
- 稀疏特性 /
- 结构特性
Abstract: While the use of SAR Tomography (TomoSAR) based on Compressive Sensing (CS) makes it possible to reconstruct the height profile of an observed scene, the performance of the reconstruction decreases for a structural observed scene. To deal with this issue, we propose using TomoSAR based on Block Compressive Sensing (BCS), which changes the reconstruction of the structural observed scene into a BCS problem under the principles of CS. Further, the block size is established by utilizing the relationship between the characteristics of the structural observed scene and the SAR parameters, such that the BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model. Compared with existing CSTomoSAR methods, the proposed BCS-TomoSAR method makes better use of the sparsity and structure information of a structural observed scene, and has higher precision and better reconstruction performance. We used simulations and Radarsat-2 data to verify the effectiveness of this proposed method.
- SAR Tomography (TomoSAR) /
- Compressive Sensing (CS) /
- Block Compressive Sensing (BCS) /
- Sparsity /
- Structure

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参考文献(26)

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