Fetching the paper…
Reading the bibliography…
Recent years have witnessed the great success of deep learning algorithms in the geoscience and remote sensing realm.
L. M. Bruce, C. H. Koger, and J. Li, “Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction,” IEEE Trans. Geos. Remote Sens. , vol. 40, no. 10, pp. 2331–2338, 2002
2002
Earlier work this paper cites.
H. Demirel, C. Ozcinar, and G. Anbarjafari, “Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition,” IEEE Geosci. Remote Sens. Lett. , vol. 7, no. 2, pp. 333–337, 2009
2009
Earlier work this paper cites.
L. Chun-Lin, A Tutorial of the Wavelet Transform . Department of Electrical Engineering, National Taiwan University, Taiwan, 2010
2010
Earlier work this paper cites.
Y. Yang and S. Newsam, “Bag-of-visual-words and spatial extensions for land-use classification,” in Proc. SIGSPATIAL Int. Conf. Adv. Geogr. Inf. Syst. , 2010, pp. 270–279
2010
Earlier work this paper cites.
M. Cramer, “The DGPF-test on digital airborne camera evaluation overview and test design,” PFG Photogrammetrie, Fernerkundung, Geoinformation , pp. 73–82, 2010
2010
Earlier work this paper cites.
A. Karami, M. Yazdi, and G. Mercier, “Compression of hyperspectral images using discerete wavelet transform and tucker decomposition,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol. 5, no. 2, pp. 444–450, 2012
2012
Earlier work this paper cites.
S. Prasad, W. Li, J. E. Fowler, and L. M. Bruce, “Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification,” IEEE Trans. Geos. Remote Sens. , vol. 50, no. 9, pp. 3474–3486, 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Proc. Neural Inf. Process. Syst. , vol. 25, pp. 1097–1105, 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
X. Guo, X. Huang, and L. Zhang, “Three-dimensional wavelet texture feature extraction and classification for multi/hyperspectral imagery,” IEEE Geosci. Remote Sens. Lett. , vol. 11, no. 12, pp. 2183–2187, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Volpi and V. Ferrari, “Semantic segmentation of urban scenes by learning local class interactions,” in Proc. IEEE Int. Conf. Comput. Vis. Workshops , 2015, pp. 1–9
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent. Springer, 2015, pp. 234–241
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 Geosci. Remote Sens. Mag. , vol. 4, no. 2, pp. 22–40, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 2818–2826
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 770–778
2016
Earlier work this paper cites.
M. Treml, J. Arjona-Medina, T. Unterthiner, R. Durgesh, F. Friedmann, P. Schuberth, A. Mayr, M. Heusel, M. Hofmarcher, M. Widrich et al. , “Speeding up semantic segmentation for autonomous driving,” in Proc. Neural Inf. Process. Syst. Workshops , 2016
2016
Earlier work this paper cites.
P. Ghamisi, N. Yokoya, J. Li, W. Liao, S. Liu, J. Plaza, B. Rasti, and A. Plaza, “Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art,” IEEE Geosci. Remote Sens. Mag. , vol. 5, no. 4, pp. 37–78, 2017
2017
Earlier work this paper cites.
G. Cheng, J. Han, and X. Lu, “Remote sensing image scene classification: Benchmark and state of the art,” Proc. IEEE , vol. 105, no. 10, pp. 1865–1883, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
G.-S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, and X. Lu, “AID: A benchmark data set for performance evaluation of aerial scene classification,” IEEE Trans. Geosci. Remote Sens. , vol. 55, no. 7, pp. 3965–3981, 2017
2017
Cited alongside, same era.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 1492–1500
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 4700–4708
2017
Cited alongside, same era.
J. Ding, N. Xue, G.-S. Xia, X. Bai, W. Yang, M. Yang, S. Belongie, J. Luo, M. Datcu, M. Pelillo et al. , “Object detection in aerial images: A large-scale benchmark and challenges,” IEEE Trans. Pattern Anal. Mach. Intell. , 2021
2021
Later among the works it cites.
Y. Xu, B. Du, and L. Zhang, “Assessing the threat of adversarial examples on deep neural networks for remote sensing scene classification: Attacks and defenses,” IEEE Trans. Geos. Remote Sens. , vol. 59, no. 2, pp. 1604–1617, 2021
2021
Later among the works it cites.
L. Chen, Z. Xu, Q. Li, J. Peng, S. Wang, and H. Li, “An empirical study of adversarial examples on remote sensing image scene classification,” IEEE Trans. Geos. Remote Sens. , vol. 59, no. 9, pp. 7419–7433, 2021
2021
Later among the works it cites.
G. Cheng, X. Sun, K. Li, L. Guo, and J. Han, “Perturbation-seeking generative adversarial networks: A defense framework for remote sensing image scene classification,” IEEE Trans. Geos. Remote Sens. , vol. 60, pp. 1–11, 2021
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 40, no. 4, pp. 834–848, 2017
2017
Cited alongside, same era.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 12, pp. 2481–2495, 2017
2017
Cited alongside, same era.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 2881–2890
2017
Cited alongside, same era.
A. Chaurasia and E. Culurciello, “LinkNet: Exploiting encoder representations for efficient semantic segmentation,” in Proc. IEEE Vis. Commun. Image Process. , 2017, pp. 1–4
2017
Cited alongside, same era.
T. Pohlen, A. Hermans, M. Mathias, and B. Leibe, “Full-resolution residual networks for semantic segmentation in street scenes,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 4151–4160
2017
Cited alongside, same era.
G. Cheng, C. Yang, X. Yao, L. Guo, and J. Han, “When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative cnns,” IEEE Trans. Geosci. Remote Sens. , vol. 56, no. 5, pp. 2811–2821, 2018
2018
Cited alongside, same era.
W. Czaja, N. Fendley, M. Pekala, C. Ratto, and I.-J. Wang, “Adversarial examples in remote sensing,” in Proc. SIGSPATIAL Int. Conf. Adv. Geogr. Inf. Syst. , 2018, pp. 408–411
2018
Cited alongside, same era.
A. Ortiz, O. Fuentes, D. Rosario, and C. Kiekintveld, “On the defense against adversarial examples beyond the visible spectrum,” in Proc. IEEE Mil. Commun. Conf. , 2018, pp. 1–5
2018
Cited alongside, same era.
Later among the works it cites.
2021
Later among the works it cites.
S. Park, H. J. Lee, and Y. M. Ro, “Adversarially robust hyperspectral image classification via random spectral sampling and spectral shape encoding,” IEEE Access , vol. 9, pp. 66 791–66 804, 2021
2021
Later among the works it cites.
Y. Xu, B. Du, and L. Zhang, “Self-attention context network: Addressing the threat of adversarial attacks for hyperspectral image classification,” IEEE Trans. Image Process. , vol. 30, pp. 8671–8685, 2021
2021
Later among the works it cites.
C. Shi, Y. Dang, L. Fang, Z. Lv, and M. Zhao, “Hyperspectral image classification with adversarial attack,” IEEE Geosci. Remote Sens. Lett. , vol. 19, pp. 1–5, 2021
2021
Later among the works it cites.
N. Akhtar, A. Mian, N. Kardan, and M. Shah, “Advances in adversarial attacks and defenses in computer vision: A survey,” IEEE Access , vol. 9, pp. 155 161–155 196, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
K. Doan, Y. Lao, W. Zhao, and P. Li, “LIRA: Learnable, imperceptible and robust backdoor attacks,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 11 966–11 976
2021
Later among the works it cites.
L. Zhang and L. Zhang, “Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities,” IEEE Geosci. Remote Sens. Mag. , vol. 10, no. 2, pp. 270–294, 2022
2022
Closest in time.
Y. Xu, W. Yu, and P. Ghamisi, “Task-guided denoising network for adversarial defense of remote sensing scene classification,” in Proc. Int. Joint Conf. Artif. Intell. Workshop , 2022
2022
Closest in time.
B. Peng, B. Peng, J. Zhou, J. Xia, and L. Liu, “Speckle-variant attack: Toward transferable adversarial attack to SAR target recognition,” IEEE Geosci. Remote Sens. Lett. , vol. 19, pp. 1–5, 2022
2022
Closest in time.
W. Xia, Z. Liu, and Y. Li, “Sar-pega: A generation method of adversarial examples for SAR image target recognition network,” IEEE Trans. Aerosp. Electron. Syst. , pp. 1–11, 2022
2022
Closest in time.
Y. Li, Y. Jiang, Z. Li, and S.-T. Xia, “Backdoor learning: A survey,” IEEE Trans. Neural Netw. Learn. Syst. , pp. 1–18, 2022
2022
Closest in time.
E. Brewer, J. Lin, and D. Runfola, “Susceptibility & defense of satellite image-trained convolutional networks to backdoor attacks,” Information Sciences , vol. 603, pp. 244–261, 2022
2022
Closest in time.
X. Sun, G. Cheng, H. Li, L. Pei, and J. Han, “Exploring effective data for surrogate training towards black-box attack,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 15 355–15 364
2022
Closest in time.
X. Sun, G. Cheng, L. Pei, and J. Han, “Query-efficient decision-based attack via sampling distribution reshaping,” Pattern Recognition , vol. 129, p. 108728, 2022
2022
Closest in time.
Y. Xu and P. Ghamisi, “Universal adversarial examples in remote sensing: Methodology and benchmark,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–15, 2022
2022
Closest in time.
Y. Feng, B. Ma, J. Zhang, S. Zhao, Y. Xia, and D. Tao, “FIBA: Frequency-injection based backdoor attack in medical image analysis,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 20 876–20 885
2022
Closest in time.
Y. Xu and P. Ghamisi, “Consistency-regularized region-growing network for semantic segmentation of urban scenes with point-level annotations,” IEEE Trans. Image Process. , vol. 31, pp. 5038–5051, 2022
2022
Closest in time.