IEEE Geoscience and Remote Sensing Magazine - June 2023 - 81

[9] G. Cheng, J. Han, and X. Lu, " Remote sensing image scene
classification: Benchmark and state of the art, " Proc.
vol. 105, no. 10, pp. 1865-1883, Mar. 2017, doi: 10.1109/
JPROC.2017.2675998.
[10] J. Ding et al., " Object detection in aerial images: A large-scale
benchmark and challenges, " IEEE Trans. Pattern Anal. Mach. Intell.,
vol. 44, no. 11, pp. 7778-7796, Nov. 2022, doi: 10.1109/
TPAMI.2021.3117983.
[11] Y. Xu et al., " Advanced multi-sensor optical remote sensing for
urban land use and land cover classification: Outcome of the
2018 IEEE GRSS data fusion contest, " IEEE J. Sel. Topics Appl.
Earth Observ. Remote Sens., vol. 12, no. 6, pp. 1709-1724, Jun.
2019, doi: 10.1109/JSTARS.2019.2911113.
[12] H. Chen, C. Wu, B. Du, L. Zhang, and L. Wang, " Change detection
in multisource VHR images via deep Siamese convolutional
multiple-layers recurrent neural network, " IEEE Trans.
Geosci. Remote Sens., vol. 58, no. 4, pp. 2848-2864, Apr. 2020,
doi: 10.1109/TGRS.2019.2956756.
[13] J. Shao, B. Du, C. Wu, M. Gong, and T. Liu, " HRSiam: High-resolution
Siamese network, towards space-borne satellite video
tracking, " IEEE Trans. Image Process., vol. 30, pp. 3056-3068,
Feb. 2021, doi: 10.1109/TIP.2020.3045634.
[14] X. Lu, B. Wang, X. Zheng, and X. Li, " Exploring models and
data for remote sensing image caption generation, " IEEE Trans.
Geosci. Remote Sens., vol. 56, no. 4, pp. 2183-2195, Apr. 2018,
doi: 10.1109/TGRS.2017.2776321.
[15] Y. Xu, W. Yu, P. Ghamisi, M. Kopp, and S. Hochreiter, " Txt2ImgMHN:
Remote sensing image generation from text using modern
Hopfield networks, " 2022, arXiv:2208.04441.
[16] S. Lobry, D. Marcos, J. Murray, and D. Tuia, " RSVQA: Visual
question answering for remote sensing data, " IEEE Trans. Geosci.
Remote Sens., vol. 58, no. 12, pp. 8555-8566, Dec. 2020, doi:
10.1109/TGRS.2020.2988782.
[17] D. Rashkovetsky, F. Mauracher, M. Langer, and M. Schmitt,
" Wildfire detection from multisensor satellite imagery using
deep semantic segmentation, " IEEE J. Sel. Topics Appl. Earth
Observ. Remote Sens., vol. 14, pp. 7001-7016, Jun. 2021, doi:
10.1109/JSTARS.2021.3093625.
[18] O. Ghorbanzadeh et al., " The outcome of the 2022 Landslide4Sense
competition: Advanced landslide detection from multisource
satellite imagery, " IEEE J. Sel. Topics Appl. Earth Observ.
Remote Sens., vol. 15, pp. 9927-9942, Nov. 2022, doi: 10.1109/
JSTARS.2022.3220845.
[19] S. Dewitte, J. P. Cornelis, R. Müller, and A. Munteanu, " Artificial
intelligence revolutionises weather forecast, climate monitoring
and decadal prediction, " Remote Sens., vol. 13, no. 16,
Aug. 2021, Art. no. 3209, doi: 10.3390/rs13163209.
[20] X. Feng, T.-M. Fu, H. Cao, H. Tian, Q. Fan, and X. Chen, " Neural
network predictions of pollutant emissions from open
burning of crop residues: Application to air quality forecasts
in southern China, " Atmos. Environ., vol. 204, pp. 22-31, May
2019, doi: 10.1016/j.atmosenv.2019.02.002.
[21] N. Jean, M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S.
Ermon, " Combining satellite imagery and machine learning to
predict poverty, " Science, vol. 353, no. 6301, pp. 790-794, Aug.
2016, doi: 10.1126/science.aaf7894.
JUNE 2023 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
IEEE,
[22] G. W. Gella et al., " Mapping of dwellings in IDP/refugee settlements
from very high-resolution satellite imagery using a
mask region-based convolutional neural network, " Remote
Sens., vol. 14, no. 3, Aug. 2022, Art. no. 689, doi: 10.3390/
rs14030689.
[23] 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, Jun.
2022, doi: 10.1109/MGRS.2022.3145854.
[24] Y. Ge, X. Zhang, P. M. Atkinson, A. Stein, and L. Li, " Geoscienceaware
deep learning: A new paradigm for remote sensing, " Sci.
Remote Sens., vol. 5, Jun. 2022, Art. no. 100047, doi: 10.1016/j.
srs.2022.100047.
[25] W. Czaja, N. Fendley, M. Pekala, C. Ratto, and I.-J. Wang, " Adversarial
examples in remote sensing, " in Proc. SIGSPATIAL
Int. Conf. Adv. Geographic Inf. Syst., 2018, pp. 408-411, doi:
10.1145/3274895.3274904.
[26] A. Krizhevsky, I. Sutskever, and G. E. Hinton, " ImageNet classification
with deep convolutional neural networks, " Commun.
ACM, vol. 60, no. 6, pp. 84-90, May 2017, doi: 10.1145/3065386.
[27] I. J. Goodfellow, J. Shlens, and C. Szegedy, " Explaining and harnessing
adversarial examples, " in Proc. Int. Conf. Learn. Representations,
2015.
[28] A. Kurakin, I. J. Goodfellow, and S. Bengio, " Adversarial examples
in the physical world, " in Proc. Int. Conf. Learn. Representations,
2017.
[29] A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, " Towards
deep learning models resistant to adversarial attacks, " in
Proc. Int. Conf. Learn. Representations, 2018.
[30] C. Szegedy et al., " Intriguing properties of neural networks, " in
Proc. Int. Conf. Learn. Representations, 2014.
[31] L. Chen, G. Zhu, Q. Li, and H. Li, " Adversarial example in remote
sensing image recognition, " 2020, arXiv:1910.13222.
[32] 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. Geosci. Remote
Sens., vol. 59, no. 2, pp. 1604-1617, Feb. 2021, doi: 10.1109/
TGRS.2020.2999962.
[33] Y. Xu and P. Ghamisi, " Universal adversarial examples in remote
sensing: Methodology and benchmark, " IEEE Trans.
Geosci. Remote Sens., vol. 60, pp. 1-15, Mar. 2022, doi: 10.1109/
TGRS.2022.3156392.
[34] Y. Zhou, M. Kantarcioglu, B. Thuraisingham, and B. Xi, " Adversarial
support vector machine learning, " in Proc. ACM SIGKDD
Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 1059-1067,
doi: 10.1145/2339530.2339697.
[35] T. Bai, H. Wang, and B. Wen, " Targeted universal adversarial
examples for remote sensing, " Remote Sens., vol. 14, no. 22,
Nov. 2022, Art. no. 5833, doi: 10.3390/rs14225833.
[36] 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, Oct. 2021, doi: 10.1109/TIP.2021.3118977.
[37] M. E. Paoletti, J. M. Haut, R. Fernandez-Beltran, J. Plaza, A. J.
Plaza, and F. Pla, " Deep pyramidal residual networks for spectral-spatial
hyperspectral image classification, " IEEE Trans.
81
IEEE Geoscience and Remote Sensing Magazine - June 2023

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2023
