IEEE Geoscience and Remote Sensing Magazine - December 2021 - 33

[147] Y. You, Z. Li, B. Ran, J. Cao, S. Lv, and F. Liu, " Broad area target
search system for ship detection via deep convolutional
neural network, " Remote Sens., vol. 11, no. 17, p. 1965, 2019.
doi: 10.3390/rs11171965.
[148] W. Kang, Y. Xiang, F. Wang, and H. You, " EU-Net: An efficient
fully convolutional network for building extraction from optical
remote sensing images, " Remote Sens., vol. 11, no. 23,
p. 2813, 2019. doi: 10.3390/rs11232813.
[149] N. Mo, L. Yan, R. Zhu, and H. Xie, " Class-specific anchor
based and context-guided multi-class object detection in High
Resolution Remote Sensing Imagery with a convolutional
neural network, " Remote Sens., vol. 11, no. 3, p. 272, 2019. doi:
10.3390/rs11030272.
[150] W. Xie, H. Qin, Y. Li, Z. Wang, and J. Lei, " A novel effectively
optimized one-stage network for object detection in remote
sensing imagery, " Remote Sens., vol. 11, no. 11, p. 1376, 2019.
doi: 10.3390/rs11111376.
[151] G. Cheng, P. Zhou, and J. Han, " RIFD-CNN: Rotation-invariant
and fisher discriminative convolutional neural networks
for object detection, " in Proc. IEEE Conf. Comput. Vis.
Pattern Recognit. (CVPR), 2016, pp. 2884-2893. doi: 10.1109/
CVPR.2016.315.
[152] J. Bruna and S. Mallat, " Invariant scattering convolution networks, "
IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp.
1872-1886, 2013. doi: 10.1109/TPAMI.2012.230.
[153] H. He, Y. Lin, F. Chen, H. Tai, and Z. Yin, " Inshore ship detection
in remote sensing images via weighted pose voting, " IEEE
Trans. Geosci. Remote Sens., vol. 55, no. 6, pp. 3091-3107, 2017.
doi: 10.1109/TGRS.2017.2658950.
[154] G. Cheng, J. Han, P. Zhou, and D. Xu, " Learning rotationinvariant
and Fisher discriminative convolutional neural
networks for object detection, " IEEE Trans. Image Process.,
vol. 28, no. 1, pp. 265-278, 2019. doi: 10.1109/TIP.2018.
2867198.
[155] Y. Zhou, Q. Ye, Q. Qiu, and J. Jiao, " Oriented response networks, "
in Proc.
IEEE Conf. Comput. Vis. Pattern Recognit.
(CVPR), 2017, pp. 4961-4970.
[156] K. Li, G. Cheng, S. Bu, and X. You, " Rotation-insensitive and
context-augmented object detection in remote sensing images, "
IEEE Trans. Geosci. Remote Sens., vol. 56, no. 4, pp. 2337-
2348, 2018. doi: 10.1109/TGRS.2017.2778300]
[157] M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu,
" Spatial transformer networks, " in Proc. Annu. Conf. Neural
Inf. Process. Syst., 2015, pp. 2017-2025.
[158] C. Chen, W. Gong, Y. Chen, and W. Li, " Object detection in
remote sensing images based on a scene-contextual feature
pyramid network, " Remote Sens., vol. 11, no. 3, p. 339, 2019.
doi: 10.3390/rs11030339.
[159] R. B. Girshick, F. N. Iandola, T. Darrell, and J. Malik, " Deformable
part models are convolutional neural networks, " in Proc.
IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2015, pp. 437-
446. doi: 10.1109/CVPR.2015.7298641.
[160] W. Ouyang et al., " DeepID-Net: Deformable deep convolutional
neural networks for object detection, " in Proc. IEEE Conf.
Comput. Vis. Pattern Recognit. (CVPR), 2015, pp. 2403-2412.
doi: 10.1109/CVPR.2015.7298854.
DECEMBER 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
[161] J. Dai et al., " Deformable convolutional networks, " in Proc.
IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 764-773.
[162] T. Mordan, N. Thome, G. Hénaff, and M. Cord, " End-to-end
learning of latent deformable part-based representations for
object detection, " Int. J. Comput. Vis., vol. 127, nos. 11-12, pp.
1659-1679, 2019. doi: 10.1007/s11263-018-1109-z.
[163] W. Ouyang and X. Wang, " Joint deep learning for pedestrian
detection, " in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2013,
pp. 2056-2063. doi: 10.1109/ICCV.2013.257.
[164] X. Wang, A. Shrivastava, and A. Gupta, " A-fast-RCNN: Hard
positive generation via adversary for object detection, " in Proc.
IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp.
3039-3048. doi: 10.1109/CVPR.2017.324.
[165] S. Zhang, J. Yang, and B. Schiele, " Occluded pedestrian detection
through guided attention in CNNs, " in Proc. IEEE Conf.
Comput. Vis. Pattern Recognit. (CVPR), 2018, pp. 6995-7003.
doi: 10.1109/CVPR.2018.00731.
[166] N. Dalal and B. Triggs, " Histograms of oriented gradients for
human detection, " in Proc. IEEE Comput. Soc. Conf. Comput.
Vis. Pattern Recognit. (CVPR), 2005, pp. 886-893. doi:
10.1109/CVPR.2005.177.
[167] A. Shrivastava, A. Gupta, and R. B. Girshick, " Training regionbased
object detectors with online hard example mining, " in
Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp.
761-769. doi: 10.1109/CVPR.2016.89.
[168] S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, " Single-shot refinement
neural network for object detection, " in Proc. IEEE Conf.
Comput. Vis. Pattern Recognit. (CVPR), 2018, pp. 4203-4212.
[169] G. Zhang, S. Lu, and W. Zhang, " CAD-net: A context-aware detection
network for objects in remote sensing imagery, " IEEE
Trans. Geosci. Remote Sens., vol. 57, no. 12, pp. 10,015-10,024,
2019. doi: 10.1109/TGRS.2019.2930982.
[170] J. Jin, K. Fu, and C. Zhang, " Traffic sign recognition with hinge
loss trained convolutional neural networks, " IEEE Trans. Intell.
Transp. Syst., vol. 15, no. 5, pp. 1991-2000, 2014. doi: 10.1109/
TITS.2014.2308281.
[171] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger,
" Densely connected convolutional networks, " in Proc.
IEEE
Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 2261-
2269. doi: 10.1109/CVPR.2017.243.
[172] J. Pang, K. Chen, J. Shi, H. Feng, W. Ouyang, and D. Lin, " Libra
R-CNN: Towards balanced learning for object detection, " in
Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp.
821-830. doi: 10.1109/CVPR.2019.00091.
[173] G. Cheng et al., " Object detection in remote sensing imagery
using a discriminatively trained mixture model, " ISPRS
J. Photogram. Remote Sens., vol. 85, pp. 32-43, Nov. 2013. doi:
10.1016/j.isprsjprs.2013.08.001.
[174] B. Cai, Z. Jiang, H. Zhang, D. Zhao, and Y. Yao, " Airport detection
using end-to-end convolutional neural network with hard
example mining, " Remote Sens., vol. 9, no. 11, pp. 1-20, 2017.
doi: 10.3390/rs9111198.
[175] Y. Xu, M. Zhu, S. Li, H. Feng, S. Ma, and J. Che, " End-to-end airport
detection in remote sensing images combining cascade region proposal
networks and multi-threshold detection networks, " Remote
Sens., vol. 10, no. 10, pp. 1-17, 2018. doi: 10.3390/rs10101516.
33
IEEE Geoscience and Remote Sensing Magazine - December 2021

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