IEEE Geoscience and Remote Sensing Magazine - December 2022 - 67

[129] D. G. Tzikas, A. C. Likas, and N. P. Galatsanos, " The variational
approximation for Bayesian inference, " IEEE Signal Process.
Mag., vol. 25, no. 6, pp. 131-146, Nov. 2008, doi: 10.1109/
MSP.2008.929620.
[130] L. Zhang, H. Wang, and Z. Qiao, " Resolution enhancement for
ISAR imaging via improved statistical compressive sensing, "
EURASIP J. Adv. Signal Process., vol. 2016, no. 1, pp. 1-19, Jul.
2016, doi: 10.1186/s13634-016-0379-2.
[131] J. Wan et al., " Sparse Bayesian multi-task learning for predicting
cognitive outcomes from neuroimaging measures in
Alzheimer's disease, " in Proc. 2012 IEEE Conf. Comput.
Vis. Pattern Recognit., pp. 940-947, doi: 10.1109/CVPR.2012.
6247769.
[132] Y. Bengio, " Deep learning of representations for unsupervised
and transfer learning, " in Proc. 2012 ICML Workshop Unsupervised
Transfer Learn. JMLR Workshop Conf., pp. 17-36.
[133] J. Schmidhuber, " Deep learning in neural networks: An overview, "
Neural Netw., vol. 61, pp. 85-117, Jan. 2015, doi: 10.1016/j.
neunet.2014.09.003.
[134] J. Kang, Y. Wang, M. Schmitt, and X. X. Zhu, " Object-based multipass
InSAR via robust low-rank tensor decomposition, " IEEE
Trans. Geosci. Remote Sens., vol. 56, no. 6, pp. 3062-3077, Jun.
2018, doi: 10.1109/TGRS.2018.2790480.
[135] W. Pu and J. Wu, " OSRanP: A novel way for radar imaging utilizing
joint sparsity and low-rankness, " IEEE Trans. Comput. Imag.,
vol. 6, pp. 868-882, May 2020, doi: 10.1109/TCI.2020.2993170.
[136] W. Qiu, J. Zhou, and Q. Fu, " Tensor representation for threedimensional
radar target imaging with sparsely sampled data, "
IEEE Trans. Comput. Imag., vol. 6, pp. 263-275, 2020, doi:
10.1109/TCI.2019.2948776.
[137] Y. Zhang, Y. Li, J. Chen, S. Shahir, and S. Safavi-Naeini, " Sparse
millimeter-wave InSAR imaging approach based on MC, " IEEE
Geosci. Remote Sens. Lett., vol. 15, no. 5, pp. 714-718, May 2018,
doi: 10.1109/LGRS.2018.2810234.
[138] W. Qiu, J. Zhou, and Q. Fu, " Jointly using low-rank and sparsity
priors for sparse inverse synthetic aperture radar imaging, " IEEE
Trans. Image Process., vol. 29, pp. 100-115, 2020, doi: 10.1109/
TIP.2019.2927458.
[139] G. Baier, W. He, and N. Yokoya, " Robust nonlocal low-rank
SAR time series despeckling considering speckle correlation
by total variation regularization, " IEEE Trans. Geosci. Remote
Sens., vol. 58, no. 11, pp. 7942-7954, Nov. 2020, doi: 10.1109/
TGRS.2020.2985400.
[140] Y. Zhang, D. Zhu, P. Wang, G. Zhang, and H. Leung, " Visionbased
vehicle detection for VideoSAR surveillance using lowrank
plus sparse three-term decomposition, " IEEE Trans. Veh.
Technol., vol. 69, no. 5, pp. 4711-4726, May 2020, doi: 10.1109/
TVT.2020.2978263.
[141] H. Yan, R. Wang, F. Li, Y. Deng, and Y. Liu, " Ground moving
target extraction in a multichannel wide-area surveillance SAR/
GMTI system via the relaxed PCP, " IEEE Geosci. Remote Sens.
Lett., vol. 10, no. 3, pp. 617-621, May 2013, doi: 10.1109/
LGRS.2012.2216248.
[142] J. Yin, C. Unal, M. Schleiss, and H. Russchenberg, " Radar target
and moving clutter separation based on the low-rank matrix
optimization, " IEEE Trans. Geosci. Remote Sens., vol. 56,
DECEMBER 2022 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
no. 8, pp. 4765-4780, Aug. 2018, doi: 10.1109/TGRS.2018.
2837051.
[143] M. Yu, G. Dong, H. Fan, and G. Kuang, " SAR target recognition
via local sparse representation of multi-manifold regularized
low-rank approximation, " Remote Sens., vol. 10, no. 2, p. 211,
Feb. 2018, doi: 10.3390/rs10020211.
[144] S. Ravishankar, J. C. Ye, and J. A. Fessler, " Image reconstruction:
From sparsity to data-adaptive methods and machine learning, "
Proc. IEEE, vol. 108, no. 1, pp. 86-109, Jan. 2020, doi: 10.1109/
JPROC.2019.2936204.
[145] F. Biondi, " Low-rank plus sparse decomposition and localized
radon transform for ship-wake detection in synthetic aperture
radar images, " IEEE Geosci. Remote Sens. Lett., vol. 15, no. 1, pp.
117-121, Jan. 2018, doi: 10.1109/LGRS.2017.2777264.
[146] Y. LeCun, Y. Bengio, and G. Hinton, " Deep learning, " Nature,
vol. 521, no. 7553, pp. 436-444, May 2015, doi: 10.1038/nature14539.
[147]
W. Shi, F. Jiang, S. Liu, and D. Zhao, " Image compressed sensing
using convolutional neural network, " IEEE Trans. Image Process.,
vol. 29, pp. 375-388, 2020, doi: 10.1109/TIP.2019.2928136.
[148] H. Yao, F. Dai, S. Zhang, Y. Zhang, Q. Tian, and C. Xu, " DR2-Net:
Deep residual reconstruction network for image compressive
sensing, " Neurocomputing, vol. 359, pp. 483-493, Sep. 2019, doi:
10.1016/j.neucom.2019.05.006.
[149] G. Yang et al., " DAGAN: Deep de-aliasing generative adversarial
networks for fast compressed sensing MRI reconstruction, " IEEE
Trans. Med. Imag., vol. 37, no. 6, pp. 1310-1321, Jun. 2018, doi:
10.1109/TMI.2017.2785879.
[150] A. Mousavi, A. B. Patel, and R. G. Baraniuk, " A deep learning
approach to structured signal recovery, " in Proc. 2015 53rd Annu.
Allerton Conf. Commun., Control, Comput. (Allerton), pp. 1336-
1343, doi: 10.1109/ALLERTON.2015.7447163.
[151] L. Zhou, H. Yu, and Y. Lan, " Deep convolutional neural networkbased
robust phase gradient estimation for two-dimensional
phase unwrapping using SAR interferograms, " IEEE Trans. Geosci.
Remote Sens., vol. 58, no. 7, pp. 4653-4665, Jul. 2020, doi:
10.1109/TGRS.2020.2965918.
[152] Q. Cheng, A. A. Ihalage, Y. Liu, and Y. Hao, " Compressive sensing
radar imaging with convolutional neural networks, " IEEE
Access, vol. 8, pp. 212,917-212,926, 2020, doi: 10.1109/ACCESS.
2020.3040498.
[153] E. Mason, B. Yonel, and B. Yazici, " Deep learning for SAR image
formation, " in Proc. 24th Algorithms Synthetic Aperture Radar Imagery,
2017, vol. 10201, p. 1,020,104.
[154] X. Bai, R. Xue, L. Wang, and F. Zhou, " Sequence SAR image classification
based on bidirectional convolution-recurrent network, "
IEEE Trans. Geosci. Remote Sens., vol. 57, no. 11, pp. 9223-9235,
Nov. 2019, doi: 10.1109/TGRS.2019.2925636.
[155] F. Zhou, L. Wang, X. Bai, and Y. Hui, " SAR ATR of ground vehicles
based on LM-BN-CNN, " IEEE Trans. Geosci. Remote
Sens., vol. 56, no. 12, pp. 7282-7293, Dec. 2018, doi: 10.1109/
TGRS.2018.2849967.
[156] Y. Kim and T. Moon, " Human detection and activity classification
based on micro-Doppler signatures using deep convolutional
neural networks, " IEEE Geosci. Remote Sens. Lett., vol. 13,
no. 1, pp. 8-12, Jan. 2016, doi: 10.1109/LGRS.2015.2491329.
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