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Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation.
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H. Ren and C.-I. Chang, “Automatic spectral target recognition in hyperspectral imagery,” IEEE Transactions on Aerospace and Electronic Systems , vol. 39, no. 4, pp. 1232–1249, Oct. 2003
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2009
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2010
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Y. Zhang, B. Du, and L. Zhang, “A sparse representation-based binary hypothesis model for target detection in hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 3, pp. 1346–1354, Aug. 2014
2014
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2014
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W. Li, Q. Du, and B. Zhang, “Combined sparse and collaborative representation for hyperspectral target detection,” Pattern Recognition , vol. 48, no. 12, pp. 3904–3916, Jun. 2015
2015
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2015
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J. An and S. Cho, “Variational autoencoder based anomaly detection using reconstruction probability,” Special lecture on IE , vol. 2, no. 1, pp. 1–18, Dec. 2015
2015
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2015
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, Nov. 2015, pp. 234–241
2015
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2016
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T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Nov. 2017, pp. 2117–2125
2017
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2018
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T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European journal of operational research , vol. 270, no. 2, pp. 654–669, Oct. 2018
2018
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M. Shimoni, R. Haelterman, and C. Perneel, “Hypersectral imaging for military and security applications: Combining myriad processing and sensing techniques,” IEEE Geoscience and Remote Sensing Magazine , vol. 7, no. 2, pp. 101–117, Jun. 2019
2019
Cited alongside, same era.
Y. Shi, J. Lei, Y. Yin, K. Cao, Y. Li, and C.-I. Chang, “Discriminative feature learning with distance constrained stacked sparse autoencoder for hyperspectral target detection,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 9, pp. 1462–1466, Mar. 2019
2019
J. Zeng and Q. Wang, “Sparse tensor model-based spectral angle detector for hyperspectral target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–15, Sep. 2022
2022
Later among the works it cites.
Y. Wang, X. Chen, F. Wang, M. Song, and C. Yu, “Meta-learning based hyperspectral target detection using siamese network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, Apr. 2022
2022
Later among the works it cites.
D. Zhu, B. Du, Y. Dong, and L. Zhang, “Target detection with spatial-spectral adaptive sample generation and deep metric learning for hyperspectral imagery,” IEEE Transactions on Multimedia , vol. 25, pp. 6538–6550, 2022
2022
Later among the works it cites.
Y. Li, Y. Shi, K. Wang, B. Xi, J. Li, and P. Gamba, “Target detection with unconstrained linear mixture model and hierarchical denoising autoencoder in hyperspectral imagery,” IEEE Transactions on Image Processing , vol. 31, pp. 1418–1432, Jan. 2022
2022
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Cited alongside, same era.
B. Zhang and R. Sennrich, “Root mean square layer normalization,” Advances in Neural Information Processing Systems , vol. 32, Dec. 2019
2019
Cited alongside, same era.
F. Vincent and O. Besson, “One-step generalized likelihood ratio test for subpixel target detection in hyperspectral imaging,” IEEE Transactions on Geoscience and Remote Sensing , vol. 58, no. 6, pp. 4479–4489, Jun. 2020
2020
Cited alongside, same era.
G. Zhang, S. Zhao, W. Li, Q. Du, Q. Ran, and R. Tao, “Htd-net: A deep convolutional neural network for target detection in hyperspectral imagery,” Remote Sensing , vol. 12, no. 9, p. 1489, May 2020
2020
Cited alongside, same era.
D. Zhu, B. Du, and L. Zhang, “Two-stream convolutional networks for hyperspectral target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 8, pp. 6907–6921, Nov. 2020
2020
Cited alongside, same era.
W. Xie, X. Zhang, Y. Li, K. Wang, and Q. Du, “Background learning based on target suppression constraint for hyperspectral target detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 13, pp. 5887–5897, Sep. 2020
2020
Cited alongside, same era.
C.-I. Chang, “An effective evaluation tool for hyperspectral target detection: 3d receiver operating characteristic curve analysis,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 6, pp. 5131–5153, Sep. 2020
2020
Cited alongside, same era.
T. Cheng and B. Wang, “Decomposition model with background dictionary learning for hyperspectral target detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 14, pp. 1872–1884, Jan. 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Later among the works it cites.
W. Rao, L. Gao, Y. Qu, X. Sun, B. Zhang, and J. Chanussot, “Siamese transformer network for hyperspectral image target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–19, Mar. 2022
2022
Later among the works it cites.
L. Girard, V. Roy, T. Eude, and P. Giguère, “Swin transformer for hyperspectral rare sub-pixel target detection,” in Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII , vol. 12094. SPIE, May 2022, pp. 262–270
2022
Later among the works it cites.
S. Karim, A. Qadir, U. Farooq, M. Shakir, and A. A. Laghari, “Hyperspectral imaging: a review and trends towards medical imaging,” Current medical imaging , vol. 19, no. 5, pp. 417–427, Apr. 2023
2023
Later among the works it cites.
C. B. Pande and K. N. Moharir, “Application of hyperspectral remote sensing role in precision farming and sustainable agriculture under climate change: A review,” Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems , pp. 503–520, Feb. 2023
2023
Later among the works it cites.
B. Chen, L. Liu, Z. Zou, and Z. Shi, “Target detection in hyperspectral remote sensing image: Current status and challenges,” Remote Sensing , vol. 15, no. 13, p. 3223, Jun. 2023
2023
Later among the works it cites.
J. Jiao, Z. Gong, and P. Zhong, “Triplet spectralwise transformer network for hyperspectral target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–17, Aug. 2023
2023
Later among the works it cites.
Y. Wang, X. Chen, E. Zhao, and M. Song, “Self-supervised spectral-level contrastive learning for hyperspectral target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–15, Apr. 2023
2023
Later among the works it cites.
Y. Li, H. Qin, and W. Xie, “Htdformer: Hyperspectral target detection based on transformer with distributed learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–15, Sep. 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Sun, L. Zhuang, L. Gao, H. Gao, X. Sun, and B. Zhang, “Information retrieval with chessboard-shaped topology for hyperspectral target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–15, Jun. 2023
2023
Later among the works it cites.
P. Liu, T. Xu, H. Chen, S. Zhou, H. Qin, and J. Li, “Spectrum-driven mixed-frequency network for hyperspectral salient object detection,” IEEE Transactions on Multimedia , vol. 26, pp. 5296–5310, Nov. 2024
2024
Closest in time.
Q. Tian, C. He, Y. Xu, Z. Wu, and Z. Wei, “Hyperspectral target detection: Learning faithful background representations via orthogonal subspace-guided variational autoencoder,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–14, Apr. 2024
2024
Closest in time.