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javadzade M, Kahaei M H, Beheshti Shirazi A. Fast Reconstruction of SAR Images with Phase Error Using Sparse Representation. JSDP 2022; 19 (2) : 10
URL: http://jsdp.rcisp.ac.ir/article-1-1109-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1109-en.html
Abstract: (991 Views)
In the past years, a number of algorithms have been introduced for synthesis aperture radar (SAR) imaging. However, they all suffer from the same problem: The data size to process is considerably large. In recent years, compressive sensing and sparse representation of the signal in SAR have gained a significant research interest. This method offers the advantage of reducing the sampling rate but also suffers from speed processing limitation and it needs a huge amount of memory to reconstruct the image. On the other hand, inaccuracy in SAR model induces phase error to the results and makes the reconstructed image blurry. Existing sparse methods in the presence of phase error, have high computational costs and need a lot of processing time. In addition, these methods take up considerable space in the memory for saving the measurement matrix. In this paper, a fast method is proposed to reduce the computational cost of image reconstruction, based on the signal sparsity in the presence of phase error. The proposed method consists of substituting accurate observations of sparsity methods with approximated observations of matched filter methods. In this method, the output of Range-Doppler matched filter is reconstructed with sparse representation, and error phase is estimated simultaneously. This method leads to a nonconvex optimization problem and to solve that, we use the majorization minimization method. The phase error and reconstructed image are estimated in an iterative procedure. The use of approximated observation, eliminates the need for carrying out big matrix multiplications, and Fast Fourier Transformation, as a low computational cost operation, can be employed instead. In addition to computation speed, this method does not need any memory space for saving measurement matrices.
In our numerical simulations, we compared the speed of processing and the mean square error (MSE) of reconstructed images for the proposed method with the state-of-the-art sparse method for different sizes of image and under-sampling rates. It is shown in simulations that the reconstructed image from our method has a slightly lower quality and higher MSE, because of the sidelobes effect of the matched filter output. However, in certain conditions, the speed of the proposed method is more than a hundred times faster than the compared method. The achieved processing speed with no need for the memory to store the measurement matrix at the expense of slightly lower image quality would be acceptable for most applications.
In our numerical simulations, we compared the speed of processing and the mean square error (MSE) of reconstructed images for the proposed method with the state-of-the-art sparse method for different sizes of image and under-sampling rates. It is shown in simulations that the reconstructed image from our method has a slightly lower quality and higher MSE, because of the sidelobes effect of the matched filter output. However, in certain conditions, the speed of the proposed method is more than a hundred times faster than the compared method. The achieved processing speed with no need for the memory to store the measurement matrix at the expense of slightly lower image quality would be acceptable for most applications.
Article number: 10
Type of Study: Research |
Subject:
Paper
Received: 2020/01/9 | Accepted: 2022/05/11 | Published: 2022/09/30 | ePublished: 2022/09/30
Received: 2020/01/9 | Accepted: 2022/05/11 | Published: 2022/09/30 | ePublished: 2022/09/30
References
1. [1] X. Mao and D. Zhu, "Two-dimensional Autofocus for Spotlight SAR Polar Format Imagery", Computational Imaging, IEEE Transactions on, vol. 2, no. 4, pp. 524-539, December. 2016. [DOI:10.1109/TCI.2016.2612945]
2. [2] M. Behzad Fallahpour, H. Dehghani, A. Jabbar Rashidi, A. Sheikhi, "Modelling and Software Implementation of SAR Imaging System Performance in Spotlight Mode", Signal and Data Processing, vol. 13, no. 4, pp. 3-18, 2017. [DOI:10.18869/acadpub.jsdp.13.4.3]
3. [3] C. V. Jakowatz, D. E. Wahl, P. H. Eichel, D. C. Ghiglia, and P. A. Thompson, "Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach", Springer Science & Business Media, 2012.
4. [4] W. Carrara, R. Goodman, and R. Majewski, "Spotlight-Mode Synthetic Aperture Radar", Signal Processing Algorithms, Artech House, 1995.
5. [5] D. Wahl, P. Eichel, D. Ghiglia, and C. Jakowatz, "Phase gradient autofocus-a robust tool for high resolution SAR phase correction", Aerospace and Electronic Systems, IEEE Transactions on, vol.30, no.4, pp 827-835, July 1994. [DOI:10.1109/7.303752]
6. [6] R. Morrison, M. N. Do, and D. Munson, "MCA: A multichannel approach to SAR autofocus", Image Processing, IEEE Transaction on, vol.18, no.4, pp.840-853, April 2009. [DOI:10.1109/TIP.2009.2012883] [PMID]
7. [7] S. Kelly, M. Yaghoobi, and M. Davies, "Sparsity-based autofocus for undersampled synthetic aperture radar", Aerospace and Electronic Systems, IEEE Transactions on, vol.50, no.2, pp.972-986, 2014. [DOI:10.1109/TAES.2014.120502]
8. [8] G. Ian, and H. Frank Wong, "Digital processing of synthetic aperture radar data", Artech house 2005.
9. [9] N. Ö. Önhon, and M. Çetin, "A sparsity-driven approach for joint SAR imaging and phase error correction", Image Processing, IEEE Transactions on, vol.21, no.4, pp.2075-2088, 2012. [DOI:10.1109/TIP.2011.2179056] [PMID]
10. [10] J. Fang, Z. Xu, B. Zhang, W. Hong, and Y. Wu, "Fast compressed sensing SAR imaging based on approximated observation", Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.7, no.1, pp.352-363, 2014. [DOI:10.1109/JSTARS.2013.2263309]
11. [11] S. Uḡur, O. Arıkan, and A. Cafer Gürbüz, "SAR image reconstruction by expectation maximization based matching pursuit", Digital Signal Processing, vol 37, pp.75-84, 2015. [DOI:10.1016/j.dsp.2014.11.001]
12. [12] D. W. Warner, D. Ghiglia, A. FitzGerrell, and J. Beaver, "Two-dimensional phase gradient autofocus," International Symposium on Optical Science and Technology. International Society for Optics and Photonics, vol.4123, pp.162-173, 2000. [DOI:10.1117/12.409267]
13. [13] M. Yaghoobi, T. Blumensath, and M. Davies, "Dictionary learning for sparse approximations with the majorization method", Signal Processing, IEEE Transaction on, vol.57, no.6, pp.2178-2191, June 2009. [DOI:10.1109/TSP.2009.2016257]
14. [14] A. S. Khwaja, L. Ferro-Famil, and E. Pottier, "SAR raw data simulation using high precision focusing methods," in Proc. Eur. Radar Conf. (EuRAD), pp. 33-36, 2005. [DOI:10.1109/EURAD.2006.280328]
15. [15] G. Franceschetti, R. Guida, A. Iodice, D. Riccio, and G. Ruello, "Efficient simulation of hybrid stripmap/spotlight SAR raw signals from extended scenes," Geosci. Remote Sens, IEEE Transaction on, vol.42, no.11, pp.2385-2396, 2004. [DOI:10.1109/TGRS.2004.834763]
16. [16] X. Dong, and Y. Zhang, "A novel compressive sensing algorithm for SAR imaging", Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.7, no.2, pp.708-720, 2014. [DOI:10.1109/JSTARS.2013.2291578]
17. [17] M. Schlutz, "synthetic Aperture Radar Imaging Simulated in Matlab", M.S Thesis of California Polytechnic State University, San Luis Obispo, 2009.
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