SCIENCE CHINA Information Sciences, Volume 60, Issue 10: 102305(2017) https://doi.org/10.1007/s11432-016-9017-6

Compressed sensing application in interferometric synthetic aperture radar

More info
  • ReceivedJul 9, 2016
  • AcceptedJan 21, 2017
  • PublishedJun 22, 2017


A novel interferometric synthetic aperture radar (InSAR) signal processing method based on compressed sensing (CS) theory is investigated in this paper. InSAR image formation provides the scene reflectivity estimation along azimuth and range coordinates with the height information. While surveying the height information of the illuminated scene, the data volume enlarges. CS theory allows sparse sampling during the data acquisition, which can reduce the data volume and release the pressure on the record devices. InSAR system which configures two antennas to cancel the common backscatter random phase in each resolution element implies the sparse nature of the complex-valued InSAR image. The complex-valued image after conjugate multiplication that only a phase term proportional to the differential path delay is left becomes sparse in the transform domain. Sparse sampling such as M-sequence can be implemented during the data acquisition. CS theory can be introduced to the processing due to the sparsity and a link between raw data and interferometric complex-valued image can be built. By solving the CS inverse problem, the magnitude image and interferometric phase are generated at the same time. Results on both the simulated data and real data are presented. In comparison with the conventional SAR interferometry processing results, CS-based method shows the ability to keep the imaging quality with less data acquisition.


This work was supported by National Natural Science Foundation of China (Grant No. 61271422).


[1] Graham L C. Synthetic interferometer radar for topographic mapping. Proc IEEE, 1974, 62: 763-768 CrossRef Google Scholar

[2] Al-Shuaibi A. The Riemann zeta function used in the inversion of the Laplace transform. Inverse Problems, 1998, 14: 1-7 CrossRef ADS Google Scholar

[3] Allen C T. Interferometric synthetic aperture radar. IEEE Geosci Remote Sens Soc Newslett, 1995, 96: 6--13. Google Scholar

[4] Berardino P, Fornaro G, Lanari R. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens, 2002, 40: 2375-2383 CrossRef ADS Google Scholar

[5] Donoho D L. Compressed sensing. IEEE Trans Inform Theor, 2006, 52: 1289-1306 CrossRef Google Scholar

[6] Candes E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theor, 2006, 52: 489-509 CrossRef Google Scholar

[7] Baraniuk R, Steeghs P. Compressive radar imaging. In: Prcoeedings of IEEE Radar Conference, Boston, 2007. 128--133. Google Scholar

[8] Lei Zhang , Mengdao Xing , Cheng-Wei Qiu . Achieving Higher Resolution ISAR Imaging With Limited Pulses via Compressed Sampling. IEEE Geosci Remote Sens Lett, 2009, 6: 567-571 CrossRef ADS Google Scholar

[9] Schmitt M, Stilla U. Compressive Sensing Based Layover Separation in Airborne Single-Pass Multi-Baseline InSAR Data. IEEE Geosci Remote Sens Lett, 2013, 10: 313-317 CrossRef ADS Google Scholar

[10] Budillon A, Evangelista A, Schirinzi G. Three-Dimensional SAR Focusing From Multipass Signals Using Compressive Sampling. IEEE Trans Geosci Remote Sens, 2011, 49: 488-499 CrossRef ADS Google Scholar

[11] Fornaro G, Lombardini F, Pauciullo A. Tomographic Processing of Interferometric SAR Data: Developments, applications, and future research perspectives. IEEE Signal Process Mag, 2014, 31: 41-50 CrossRef ADS Google Scholar

[12] Zhu X X, Bamler R. Superresolving SAR Tomography for Multidimensional Imaging of Urban Areas: Compressive sensing-based TomoSAR inversion. IEEE Signal Process Mag, 2014, 31: 51-58 CrossRef ADS Google Scholar

[13] Li J, Xing M D, Wu S. Application of compressed sensing in sparse aperture imaging of radar. In: Proceedings of the 2nd Asian-Pacific Conference on Synthetic Aperture Radar, Xi'an, 2009. 651--655. Google Scholar

[14] Austin C D, Ertin E, Moses R L. Sparse Signal Methods for 3-D Radar Imaging. IEEE J Sel Top Signal Process, 2011, 5: 408-423 CrossRef ADS Google Scholar

[15] Potter L C, Ertin E, Parker J T. Sparsity and Compressed Sensing in Radar Imaging. Proc IEEE, 2010, 98: 1006-1020 CrossRef Google Scholar

[16] Hou X, Yang J, Jiang G. Complex SAR Image Compression Based on Directional Lifting Wavelet Transform With High Clustering Capability. IEEE Trans Geosci Remote Sens, 2013, 51: 527-538 CrossRef ADS Google Scholar

[17] Samadi S, C?etin M, Masnadi-Shirazi M A. Sparse representation-based synthetic aperture radar imaging. IET Radar Sonar Navig, 2011, 5: 182-193 CrossRef Google Scholar

[18] Samadi S, Cetin M, Masnadi-Shirazi M A. Sparse signal representation for complex-valued imaging. In: Proceedings of IEEE 13th Digital Signal Processing Workshop and the 5th IEEE Signal Processing Education Workshop, Marco Island, 2009. 365--370. Google Scholar

[19] Ramakrishnan N, Ertin E, Moses R L. Enhancement of Coupled Multichannel Images Using Sparsity Constraints. IEEE Trans Image Process, 2010, 19: 2115-2126 CrossRef PubMed ADS Google Scholar

[20] Rosen P A, Hensley S, Joughin I R. Synthetic aperture radar interferometry. Proc IEEE, 2000, 88: 333-382 CrossRef Google Scholar

[21] Needell D, Vershynin R. Uniform Uncertainty Principle and Signal Recovery via?Regularized Orthogonal Matching Pursuit. Found Comput Math, 2009, 9: 317-334 CrossRef Google Scholar

[22] Xu Z B, Zhang H, Wang Y, et al. $L_{1/2}$ regularization. Sci China Inf Sci, 2010, 53: 1159--1169. Google Scholar

[23] Zeng J, Xu Z, Jiang H, et al. SAR imaging from compressed measurements based on $L_{1/2}$ regularization. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, 2011. 625--628. Google Scholar

[24] Bioucas-Dias J M, Figueiredo M A T. A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration. IEEE Trans Image Process, 2007, 16: 2992-3004 CrossRef ADS Google Scholar

[25] Zeng J, Xu Z, Zhang B. Accelerated regularization based SAR imaging via BCR and reduced Newton skills. Signal Processing, 2013, 93: 1831-1844 CrossRef Google Scholar

[26] Zeng J S, Fang J, Xu Z B. Sparse SAR imaging based on L 1/2 regularization. Sci China Inf Sci, 2012, 55: 1755-1775 CrossRef Google Scholar

[27] Cohn M, Lempel A. On fast M-sequence transforms (Corresp.). IEEE Trans Inform Theor, 1977, 23: 135-137 CrossRef Google Scholar

[28] Candès E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problems, 2007, 23: 969-985 CrossRef ADS Google Scholar

[29] Xu G, Xing M D, Xia X G. Sparse Regularization of Interferometric Phase and Amplitude for InSAR Image Formation Based on Bayesian Representation. IEEE Trans Geosci Remote Sens, 2015, 53: 2123-2136 CrossRef ADS Google Scholar

[30] Jinshan Zeng , Shaobo Lin , Yao Wang . $L_{1/2}$ Regularization: Convergence of Iterative Half Thresholding Algorithm. IEEE Trans Signal Process, 2014, 62: 2317-2329 CrossRef ADS arXiv Google Scholar

[31] Costantini M, Iodice A, Magnapane L, et al. Monitoring terrain movements by means of sparse SAR differential interferometric measurements. In: Proceedings of IEEE 2000 International Geoscience and Remote Sensing Symposium, Honolulu, 2000. 3225--3227. Google Scholar

[32] Meng D, Sethu V, Ambikairajah E. A Novel Technique for Noise Reduction in InSAR Images. IEEE Geosci Remote Sens Lett, 2007, 4: 226-230 CrossRef ADS Google Scholar

[33] Stevens D R, Cumming I G, Gray A L. Options for airborne interferometric SAR motion compensation. IEEE Trans Geosci Remote Sens, 1995, 33: 409-420 CrossRef ADS Google Scholar

[34] Moreira A, Mittermayer J, Scheiber R. Extended chirp scaling algorithm for air- and spaceborne SAR data processing in stripmap and ScanSAR imaging modes. IEEE Trans Geosci Remote Sens, 1996, 34: 1123-1136 CrossRef ADS Google Scholar

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有