IEEE Geoscience and Remote Sensing Magazine - December 2021 - 169

[106] C. Schwegmann, W. Kleynhans, B. Salmon, L. Mdakane, and
R. Meyer, " Very deep learning for ship discrimination in synthetic
aperture radar imagery, " in Proc. IEEE Int. Geosci. Remote
Sens. Symp. (IGARSS), 2016, pp. 104-107. doi: 10.1109/
IGARSS.2016.7729017.
[107] C. Bentes, A. Frost, D. Velotto, and B. Tings, " Ship-iceberg discrimination
with convolutional neural networks in high resolution
SAR images, " in Proc. Euro. Conf. Synth. Aperture Radar
(EUSAR), 2016, pp. 1-4.
[108] N. Ødegaard, A. Knapskog, C. Cochin, and J. Louvigne, " Classification
of ships using real and simulated data in a convolutional
neural network, " in Proc. IEEE Radar Conf. (RadarConf),
2016, pp. 1-6. doi: 10.1109/RADAR.2016.7485270.
[109] Y. Liu, M. Zhang, P. Xu, and Z. Guo, " SAR ship detection using
sea-land segmentation-based convolutional neural network, "
in Proc. Int. Workshop Remote Sens. Intell. Process. (RSIP), 2017,
pp. 1-4. doi: 10.1109/RSIP.2017.7958806.
[110] R. Girshick, " Fast R-CNN, " 2015, arXiv:1504.08083.
[111] S. Ren, K. He, R. Girshick, and J. Sun, " Faster R-CNN: Towards
real-time object detection with region proposal networks, " IEEE
Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137-1149,
2017. doi: 10.1109/TPAMI.2016.2577031.
[112] J. Li, C. Qu, and J. Shao, " Ship detection in SAR images based
on an improved faster R-CNN, " in Proc. SAR Big Data Era: Models,
Methods Appl. (BIGSARDATA), 2017, pp. 1-6, doi: 10.1109/
BIGSARDATA.2017.8124934.
[113] M. Kang, K. Ji, X. Leng, and Z. Lin, " Contextual region-based
convolutional neural network with multilayer fusion for SAR
ship detection, " Remote Sens., vol. 9, no. 8, p. 860, 2017. doi:
10.3390/rs9080860.
[114] J. Jiao et al., " A densely connected end-to-end neural network
for multiscale and multiscene SAR ship detection, " IEEE Access,
vol. 6, pp. 20,881-20,892, Apr. 2018. doi: 10.1109/ACCESS.2018.2825376.
[115]
C. Dechesne, S. Lefèvre, R. Vadaine, G. Hajduch, and R. Fablet,
" Multi-task deep learning from sentinel-1 SAR: Ship detection,
classification and length estimation, " presented at the Conf. Big
Data from Space, 2019.
[116] S. Haykin, " Cognitive radar: A way of the future, " IEEE Signal
Process. Mag., vol. 23, no. 1, pp. 30-40, 2006. doi: 10.1109/
MSP.2006.1593335.
[117] S. Kazemi, B. Yonel, and B. Yazici, " Deep learning for direct automatic
target recognition from SAR data, " in Proc. IEEE Radar
Conf. (RadarConf), 2019, pp. 1-6. doi: 10.1109/RADAR.2019.
8835492.
[118] M. Rostami, S. Kolouri, E. Eaton, and K. Kim, " Deep transfer
learning for few-shot SAR image classification, " Remote Sens.,
vol. 11, no. 11, p. 1374, 2019. doi: 10.3390/rs11111374.
[119] Z. Huang, Z. Pan, and B. Lei, " What, where, and how to transfer
in SAR target recognition based on deep CNNs, " IEEE
Trans. Geosci. Remote Sens., vol. 58, no. 4, 2019. doi: 10.1109/
TGRS.2019.2947634.
[120] M. Shahzad, M. Maurer, F. Fraundorfer, Y. Wang, and X. X. Zhu,
" Buildings detection in VHR SAR images using fully convolution
neural networks, " IEEE Trans. Geosci. Remote Sens., vol. 57,
no. 2, pp. 1100-1116, 2019. doi: 10.1109/TGRS.2018.2864716.
DECEMBER 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
[121] J. Long, E. Shelhamer, and T. Darrell, " Fully convolutional
networks for semantic segmentation, " in Proc. IEEE Int. Conf.
Comput. Vis. Pattern Recognit. (CVPR), 2015, pp. 3431-3440. doi:
10.1109/CVPR.2015.7298965.
[122] S. Zheng et al., " Conditional random fields as recurrent
neural networks, " in Proc. IEEE Int. Conf. Comput. Vis., 2015,
pp. 1529-1537. doi: 10.1109/ICCV.2015.179.
[123] Y. Sun, Y. Hua, L. Mou, and X. X. Zhu, " CG-net: Conditional
GIS-aware network for individual building segmentation in
VHR SAR images, " 2020, arXiv:2011.08362.
[124] F. Radar and J. Falkingham. " Global satellite observation requirements
for floating ice. " World Meteorological Organization.
https://globalcryospherewatch.org/satellites/docs/PSTG-4_
Doc_08-04_GlobSatObsReq-FloatingIce.pdf (accessed Jan. 25,
2021).
[125] W. Dierking, " Sea ice monitoring by synthetic aperture radar, "
Oceanography, vol. 26, no. 2, pp. 100-111, 2013. doi: 10.5670/
oceanog.2013.33.
[126] L. Wang, K. Scott, L. Xu, and D. Clausi, " Sea ice concentration
estimation during melt from dual-pol SAR scenes using
deep convolutional neural networks: A case study, " IEEE Trans.
Geosci. Remote Sens., vol. 54, no. 8, pp. 4524-4533, 2016. doi:
10.1109/TGRS.2016.2543660.
[127] L. Wang, " Learning to estimate sea ice concentration from SAR
imagery, " Ph.D. dissertation, Univ. Waterloo, 2016. [Online].
Available: http://hdl.handle.net/10012/10954
[128] S. Parrilli, M. Poderico, C. V. Angelino, and L. Verdoliva, " A
nonlocal SAR image denoising algorithm based on LLMMSE
wavelet shrinkage, " IEEE Trans. Geosci. Remote Sens., vol. 50,
no. 2, pp. 606-616, 2012. doi: 10.1109/TGRS.2011.2161586.
[129] D. Cozzolino, L. Verdoliva, G. Scarpa, and G. Poggi, " Nonlocal
CNN SAR image despeckling, " Remote Sens., vol. 12, no. 6,
p. 1006, 2020. doi: 10.3390/rs12061006.
[130] T. Song, L. Kuang, L. Han, Y. Wang, and Q. H. Liu, " Inversion of
rough surface parameters from SAR images using simulationtrained
convolutional neural networks, " IEEE Geosci. Remote
Sens. Lett., vol. 15, no. 7, pp. 1130-1134, 2018. doi: 10.1109/
LGRS.2018.2822821.
[131] J. Zhao, M. Datcu, Z. Zhang, H. Xiong, and W. Yu, " Contrastive-regulated
CNN in the complex domain: A method to learn
physical scattering signatures from flexible polsar images, " IEEE
Trans. Geosci. Remote Sens., vol. 57, no. 12, pp. 10,116-10,135,
2019. doi: 10.1109/TGRS.2019.2931620.
[132] Q. Song, F. Xu, and Y.-Q. Jin, " Radar image colorization: converting
single-polarization to fully polarimetric using deep
neural networks, " IEEE Access, vol. 6, pp. 1647-1661, 2018. [Online].
Available: https://ieeexplore.ieee.org/document/8141881
doi: 10.1109/ACCESS.2017.2779875.
[133] S. Niu, X. Qiu, B. Lei, C. Ding, and K. Fu, " Parameter extraction
based on deep neural network for SAR target simulation, " IEEE
Trans. Geosci. Remote Sens., vol. 58, no. 7, pp. 4901-4914, 2020.
doi: 10.1109/TGRS.2020.2968493.
[134] S. Auer, R. Bamler, and P. Reinartz, " RaySAR - 3D SAR simulator:
Now open source, " in Proc. IEEE Int. Geosci. Remote Sens.
Symp. (IGARSS), Beijing, 2016, pp. 6730-6733. doi: 10.1109/
IGARSS.2016.7730757.
169
https://globalcryospherewatch.org/satellites/docs/PSTG-4_Doc_08-04_GlobSatObsReq-FloatingIce.pdf https://globalcryospherewatch.org/satellites/docs/PSTG-4_Doc_08-04_GlobSatObsReq-FloatingIce.pdf http://hdl.handle.net/10012/10954 https://ieeexplore.ieee.org/document/8141881
IEEE Geoscience and Remote Sensing Magazine - December 2021

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2021
