A full-polarimetric SAR tomography method based on hierarchical sparseness

doi:10.7523/j.issn.2095-6134.2020.04.012

Abstract

Abstract: SAR tomography employs the multiple-pass data to achieve the elevation location reconstruction of the observation target, while the fully polarimetric data owns rich scattering information. We combine the full-polarimetric data with SAR tomography. By considering the same characteristic of the sparsity of elevation scatters in urban building and the sparse support set in full-polarimetric data, a solution model based on group sparse constraint and sparse constraint, solved by hierarchical sparse method, is proposed. The performance of the method has been compared with those of the single-polarimetric tomography model and the group sparse-based solution method by Monte Carlo simulation experiments. Meanwhile, the method is also applied to semi-simulation of point target experiments based on real data. The results show that the proposed method improves the accuracy of elevation reconstruction and has better robustness, and it accurately recovers the elevation position and backscatter coefficient of the target at low SNR.

Key words: SAR tomography, group sparse, sparse, full polarimetric SAR

CLC Number:

TN957.52

YANG Mudan, WEI Zhonghao, XU Zhilin, ZHANG Bingchen, HONG Wen. A full-polarimetric SAR tomography method based on hierarchical sparseness[J]. , 2020, 37(4): 525-531.

References

[1] Brown W M. Synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1967, AES-3(2):217-229.
[2] 肖垚, 刘畅. 基于稀疏求解的改进PCA方法在SAR目标识别中的应用[J]. 中国科学院大学学报, 2018, 35(1):84-88.
[3] 向卫力,李晓辉,周胜勇, 等. 一种鲁棒的多尺度稀疏表示SAR目标识别方法[J]. 中国科学院大学学报, 2017, 34(1):99-105.
[4] Bordoni F, Lombardini F, Gini F, et al. Multibaseline cross-track SAR interferometry using interpolated arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4):1473-1482.
[5] Lombard F, Ender J, RoBing L, et al. Experiments of interferometric layover solution with the three-antenna airborne AER-II SAR system[C]//IGARSS. 2004 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2004, 5:3341-3344.
[6] Pang B, Dai D H, Liu J, et al. A super-resolving imaging algorithm of forward-looking SAR based on compressive sensing[C]//IEEE Geoscience and Remote Sensing Symposium. 2014:572-575.
[7] 赵克祥, 毕辉, 张冰尘. 基于快速阈值迭代的SAR层析成像处理方法[J]. 系统工程与电子技术, 2017, 39(5):1019-1023.
[8] Zhu X, Bamler R. Super-resolution for 4-D SAR tomography via compressive sensing[C]//8th European Conference on Synthetic Aperture Radar. VDE, 2010:1-4.
[9] Zhu X X, 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.
[10] Cloude S. Polarisation:applications in remote sensing[M]. Oxford:Oxford University Press, 2010.
[11] Aguilera E, Nannini M, Reigber A. A data-adaptive compressed sensing approach to polarimetric SAR tomography of forested areas[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3):543-547.
[12] Xing S Q, Li Y Z, Dai D H, et al. Three-dimensional reconstruction of man-made objects using polarimetric tomographic SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6):3694-3705.
[13] Yuan M, Lin Y. Model selection and estimation in regression with grouped variables[J]. Journal of the Royal Statistical Society, 2006, 68(1):49-67.
[14] Liu X, Zhai D, Zhao D, et al. Image super-resolution via hierarchical and collaborative sparse representation[C]//Data Compression Conference (DCC), 2013. IEEE, 2013:93-102.
[15] Hajnsek I, Scheiber R, Ulander L et al. BioSAR 2007 technical assistance for the development of airborne SAR and geophysical measurements during the BioSAR 2007 experiment:final report without synthesis[R]. Paris:European Space Agency, 2008.