Volume 5 Issue 1
Feb.  2016
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Wang Aichun, Xiang Maosheng. SAR Tomography Based on Block Compressive Sensing[J]. Journal of Radars, 2016, 5(1): 57-64. doi: 10.12000/JR16006
Citation: Wang Aichun, Xiang Maosheng. SAR Tomography Based on Block Compressive Sensing[J]. Journal of Radars, 2016, 5(1): 57-64. doi: 10.12000/JR16006

SAR Tomography Based on Block Compressive Sensing

doi: 10.12000/JR16006
Funds:

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

  • Received Date: 2016-01-11
  • Rev Recd Date: 2016-01-27
  • Publish Date: 2016-02-28
  • 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.

     

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  • [1]
    Knaell K and Cardillo G P. Radar tomography for the generation of three-dimensional images[J]. IEEE Proceedings-Radar, Sonar and Navigation, 1995, 142(2): 54-60.
    [2]
    Reigber A and Moreira A. First demonstration of airborne SAR Tomography using multibaseline L-band data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(9): 2142-2152.
    [3]
    She Z, Gray D A, Bogner R E, et al.. Three-dimensional space-borne Synthetic Aperture Radar (SAR) imaging with multiple pass processing[J]. International Journal of Remote Sensing, 2002, 23(20): 4357-4382.
    [4]
    Gini F and Lombardini F. Multilook APES for multibaseline SAR interferometry[J]. IEEE Transactions on Signal Processing, 2002, 50(7): 1800-1803.
    [5]
    Lombardini F and Reigber A. Adaptive spectral estimation for multibaseline SAR Tomography with airborne L-band data[C]. 2003 IEEE International Geoscience and Remote Sensing Symposium IGARSS'03, Toulouse, France, 2003, 3: 2014-2016.
    [6]
    Fornaro G, Serafino F, and Soldovieri F. Three dimensional focusing with multipass SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(4): 507-517.
    [7]
    Fornaro G, Lombardini F, and Serafino F. Three-dimensional focusing multipass SAR focusing: Experiments with long-term spaceborne data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4) : 702-714.
    [8]
    Frey O and Meier E. 3-D time-domain SAR imaging of a forest using airborne multibaseline data at L-and P-bands[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3660-3664.
    [9]
    任笑真, 杨汝良. 利用FB-MAPES算法估计Tomography SAR高度维信号源数[J]. 电子与信息学报, 2009, 31(7): 1669-1673. Ren Xiao-zhen and Yang Ru-liang. On detection of number of Tomogaphy SAR signals in the elevation direction using the FB-MAPES method[J]. Journal of Electronics Information Technology, 2009, 31(7): 1669-1673.
    [10]
    吴一戎, 洪文, 张冰尘, 等. 稀疏微波成像研究进展[J]. 雷达学报, 2014, 3(4): 384-395. Wu Yi-rong, Hong Wen, Zhang Bing-chen, et al.. Current development of sparse microwave imaging[J]. Journal of Radars, 2014, 3(4): 384-395.
    [11]
    Zhu X X and Bamler R. Tomographic SAR inversion by L1-Norm regularizationThe compressive sensing approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3839-3846.
    [12]
    Donoho D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
    [13]
    Zhu X X and Bamler R. Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne Tomographic SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(1): 247-258.
    [14]
    Zhu X X and Bamler R. Superresolving SAR Tomography for multidimensional imaging of urban areas: compressive sensing-based TomoSAR inversion[J]. IEEE Signal Processing Magazine, 2014, 31(4): 51-58.
    [15]
    李烈辰, 李道京. 基于压缩感知的连续场景稀疏阵列SAR三维成像[J]. 电子与信息学报, 2014, 36(9): 2166-2172. Li Lie-chen and Li Dao-jing. Sparse array 3D imaging for continuous scene based on compressed sensing[J]. Journal of Electronics Information Technology, 2014, 36(9): 2166-2172. DOI: 10.3724/SP.J.1146.2013.01645.
    [16]
    张冰尘, 王万影, 毕辉, 等. 基于压缩多信号分类算法的森林区域极化SAR层析成像[J]. 电子与信息学报, 2015, 37(3): 625-630. Zhang Bing-chen, Wang Wan-ying, Bi Hui, et al.. Polarimetric SAR tomography for forested areas based on compressive multiple signal classification[J]. Journal of Electronics Information Technology, 2015, 37(3): 625-630.
    [17]
    廖明生, 魏恋欢, 汪紫芸, 等. 压缩感知在城区高分辨率SAR层析成像中的应用[J]. 雷达学报, 2015, 4(2): 124-129. Liao Ming-sheng, Wei Lian-huan, Wang Zi-yun, et al.. Compressive sensing in high-resolution 3D SAR Tomography of urban scenarios[J]. Journal of Radars, 2015, 4(2): 124-129.
    [18]
    Baraniuk R G, Gevher V, Duarte M F, et al.. Model-based compressive sensing[J]. IEEE Transactions on Information Theory, 2010, 56(4): 1982-2001.
    [19]
    孙洪, 张智林, 余磊. 从稀疏到结构化稀疏: 贝叶斯方法[J]. 信号处理, 2012, 28(6): 759-773. Sun Hong, Zhang Zhi-lin, and Yu Lei. From sparsity to structured sparsity: bayesian perspective[J]. Signal Processing, 2012, 28(6): 759-773.
    [20]
    李廉林, 周小阳, 崔铁军. 结构化信号处理理论和方法的研究进展[J]. 雷达学报, 2015, 4(5): 491-502. Li Lian-lin, Zhou Xiao-yang, and Cui Tie-jun. Perspectives on theories and methods of structural signal processing[J]. Journal of Radars, 2015, 4(5): 491-502.
    [21]
    Shervashidze N and Bach F. Learning the structure for structured sparsity[J]. IEEE Transactions on Signal Processing, 2015, 63(18): 4894-4902.
    [22]
    Cands E, Romberg J, and Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
    [23]
    Cands E, Romberg J, and Tao T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communication on Pure and Applied Mathematics, 2006, 59(8): 1207-1223.
    [24]
    Eldar Y C and Mishali M. Robust recovery of signals from a structured union of subspaces[J]. IEEE Transactions on Information Theory, 2009, 55(11): 5302-5316.
    [25]
    Eldar Y C, Kuppinger P, and Bolcskei H. Block-sparse signals: uncertainty relations and efficient recovery[J]. IEEE Transactions on Signal Processing, 2010, 58(6): 3042-3054.
    [26]
    Fu Y, Li H, Zhang Q, et al.. Block-sparse recovery via redundant block OMP[J]. Signal Processing, 2014, 97(7): 162-171.
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