Fetching the paper…
Reading the bibliography…
Deep learning techniques have made an increasing impact on the field of remote sensing.
B. Zitová and J. Flusser, “Image registration methods: a survey,” Image and Vision Computing , vol. 21, pp. 977–1000, 2003
2003
Earlier work this paper cites.
T. N. Sainath, A.-r. Mohamed, B. Kingsbury, and B. Ramabhadran, “Deep convolutional neural networks for lvcsr,” in 2013 IEEE international conference on acoustics, speech and signal processing . IEEE, 2013, pp. 8614–8618
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Schmidhuber and Jürgen, “Deep learning in neural networks: An overview,” Neural Netw , vol. 61, pp. 85–117, 2015
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and F.-F. Li, “Imagenet large scale visual recognition challenge,” Proc. IJCV , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Proc. NIPS , 2015, pp. 91–99
2015
Earlier work this paper cites.
L. Zhang, L. Zhang, and B. Du, “Deep learning for remote sensing data: A technical tutorial on the state of the art,” IEEE Geoscience and Remote Sensing Magazine , vol. 4, no. 2, pp. 22–40, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
Michael, Schmitt, Xiao, Xiang, and Zhu, “Data fusion and remote sensing: An ever-growing relationship,” IEEE Geoscience and Remote Sensing Magazine , vol. 4, no. 4, pp. 6–23, 2016
2016
Earlier work this paper cites.
X. X. Zhu, D. Tuia, L. Mou, G.-S. Xia, L. Zhang, F. Xu, and F. Fraundorfer, “Deep learning in remote sensing: A comprehensive review and list of resources,” IEEE Geoscience and Remote Sensing Magazine , vol. 5, no. 4, pp. 8–36, 2017
2017
Earlier work this paper cites.
J. E. Ball, D. T. Anderson, and C. S. Chan, “Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community,” Journal of Applied Remote Sensing , vol. 11, no. 4, 2017
2017
Earlier work this paper cites.
M. Schmitt, F. Tupin, and X. X. Zhu, “Fusion of sar and optical remote sensing data–challenges and recent trends,” in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . IEEE, 2017, pp. 5458–5461
2017
Earlier work this paper cites.
Q. Zhang, “System design and key technologies of the gf-3 satellite,” Acta Geodaetica et Cartographica Sinica , vol. 46, no. 3, pp. 269–277, 6 2017
2017
Cited alongside, same era.
J. Sun, W. Yu, and Y. Deng, “The sar payload design and performance for the gf-3 mission,” Sensors , vol. 17, no. 10, p. 2419, 2017
2017
Cited alongside, same era.
L. Mou, M. Schmitt, Y. Wang, and X. X. Zhu, “A cnn for the identification of corresponding patches in sar and optical imagery of urban scenes,” in 2017 Joint Urban Remote Sensing Event (JURSE) . IEEE, 2017, pp. 1–4
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Q. Feng, J. Yang, D. Zhu, J. Liu, H. Guo, B. Bayartungalag, and B. Li, “Integrating multitemporal sentinel-1/2 data for coastal land cover classification using a multibranch convolutional neural network: A case of the yellow river delta,” Remote Sensing , vol. 11, no. 9, p. 1006, 2019
2019
Later among the works it cites.
Y. Wang and X. X. Zhu, “The challenge of creating the sarptical dataset,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2019, pp. 5714–5717
2019
Later among the works it cites.
Y. Xiang, F. Wang, L. Wan, N. Jiao, and H. You, “Os-flow: A robust algorithm for dense optical and sar image registration,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 9, pp. 6335–6354, 2019
2019
Later among the works it cites.
Y. Xu, X. Xiang, and M. Huang, “Task-driven common representation learning via bridge neural network,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 5573–5580
2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
——, “The sarptical dataset for joint analysis of sar and optical image in dense urban area,” in IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2018, pp. 6840–6843
2018
Cited alongside, same era.
L. H. Hughes, M. Schmitt, L. Mou, Y. Wang, and X. X. Zhu, “Identifying corresponding patches in sar and optical images with a pseudo-siamese cnn,” IEEE Geoscience and Remote Sensing Letters , vol. 15, no. 5, pp. 784–788, 2018
2018
Cited alongside, same era.
Y. Zhang, W. Zhou, and H. Li, “Retrieval across optical and sar images with deep neural network,” in Pacific Rim Conference on Multimedia . Springer, 2018, pp. 392–402
2018
Cited alongside, same era.
2018
Cited alongside, same era.
G. Tsagkatakis, A. Aidini, K. Fotiadou, M. Giannopoulos, A. Pentari, and P. Tsakalides, “Survey of deep-learning approaches for remote sensing observation enhancement,” Sensors , vol. 19, no. 18, p. 3929, 2019
2019
Cited alongside, same era.
P. Feng, Y. Lin, J. Guan, Y. Dong, G. He, Z. Xia, and H. Shi, “Embranchment cnn based local climate zone classification using sar and multispectral remote sensing data,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2019, pp. 6344–6347
2019
Cited alongside, same era.
Z. Zhang, G. Vosselman, M. Gerke, C. Persello, D. Tuia, and M. Y. Yang, “Detecting building changes between airborne laser scanning and photogrammetric data,” Remote sensing , vol. 11, no. 20, p. 2417, 2019
2019
Cited alongside, same era.
Later among the works it cites.
S. Xian, W. Zhirui, S. Yuanrui, D. Wenhui, Z. Yue, and F. Kun, “Air-sarship–1.0: High resolution sar ship detection dataset,” J. Radars , vol. 8, no. 6, pp. 852–862, 2019
2019
Later among the works it cites.
S. C. Kulkarni and P. P. Rege, “Pixel level fusion techniques for sar and optical images: A review,” Information Fusion , vol. 59, pp. 13–29, 2020
2020
Later among the works it cites.
X. Li, L. Lei, Y. Sun, M. Li, and G. Kuang, “Multimodal bilinear fusion network with second-order attention-based channel selection for land cover classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 13, pp. 1011–1026, 2020
2020
Later among the works it cites.
J. Shermeyer, D. Hogan, J. Brown, A. Van Etten, N. Weir, F. Pacifici, R. Hansch, A. Bastidas, S. Soenen, T. Bacastow et al. , “Spacenet 6: Multi-sensor all weather mapping dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 196–197
2020
Later among the works it cites.
L. H. Hughes, D. Marcos, S. Lobry, D. Tuia, and M. Schmitt, “A deep learning framework for matching of sar and optical imagery,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 169, pp. 166–179, 2020
2020
Later among the works it cites.
W. Xiong, Z. Xiong, Y. Zhang, Y. Cui, and X. Gu, “A deep cross-modality hashing network for sar and optical remote sensing images retrieval,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 13, pp. 5284–5296, 2020
2020
Later among the works it cites.
2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation, online: http://en.sw.chreos.org
2020
Later among the works it cites.