IEEE Geoscience and Remote Sensing Magazine - March 2023 - 66

[68] R. Boyer and M. Haardt, " Noisy compressive sampling based
on block-sparse tensors: Performance limits and beamforming
techniques, " IEEE Trans. Signal Process., vol. 64, no. 23, pp.
6075-6088, Dec. 2016, doi: 10.1109/TSP.2016.2600510.
[69] R. Zhao, Q. Wang, J. Fu, and L. Ren, " Exploiting block-sparsity
for hyperspectral Kronecker compressive sensing: A tensorbased
Bayesian method, " IEEE Trans. Image Process., vol. 29, pp.
1654-1668, 2020, doi: 10.1109/TIP.2019.2944722.
[70] S. Li, W. Wang, H. Qi, B. Ayhan, C. Kwan, and S. Vance, " Lowrank
tensor decomposition based anomaly detection for hyperspectral
imagery, " in Proc. IEEE Int. Conf. Image Process., 2015,
pp. 4525-4529, doi: 10.1109/ICIP.2015.7351663.
[71] X. Zhang, G. Wen, and W. Dai, " A tensor decomposition-based
anomaly detection algorithm for hyperspectral image, " IEEE
Trans. Geosci. Remote Sens., vol. 54, no. 10, pp. 5801-5820, Oct.
2016, doi: 10.1109/TGRS.2016.2572400.
[72] L. Zhu, G. Wen, S. Qiu, and X. Zhang, " Improving hyperspectral
anomaly detection with a simple weighting strategy, " IEEE
Geosci. Remote Sens. Lett., vol. 16, no. 1, pp. 95-99, Jan. 2019,
doi: 10.1109/LGRS.2018.2869337.
[73] S. Song, H. Zhou, L. Gu, Y. Yang, and Y. Yang, " Hyperspectral
anomaly detection via tensor- based endmember extraction
and low-rank decomposition, " IEEE Geosci. Remote Sens.
Lett., vol. 17, no. 10, pp. 1772-1776, Oct. 2020, doi: 10.1109/
LGRS.2019.2953342.
[74] F. Qin, Z. Wu, Y. Xu, H. Liu, Y. Zhang, and Z. Wei, " Hyperspectral
anomaly detection based on low rank and sparse
tensor decomposition, " in Proc. IEEE Int. Geosci. Remote
Sens. Symp. (IGARSS), 2019, pp. 2240-2243, doi: 10.1109/
IGARSS.2019.8900223.
[75] Z. Chen, B. Yang, and B. Wang, " A preprocessing method for
hyperspectral target detection based on tensor principal component
analysis, " Remote Sens., vol. 10, no. 7, 2018, Art. no.
1033, doi: 10.3390/rs10071033.
[76] L. Li, W. Li, Y. Qu, C. Zhao, R. Tao, and Q. Du, " Prior-based
tensor approximation for anomaly detection in hyperspectral
imagery, " IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 3, pp.
1037-1050, Mar. 2022, doi: 10.1109/TNNLS.2020.3038659.
[77] M. Wang, Q. Wang, D. Hong, S. Roy, and J. Chanussot, " Learning
tensor low-rank representation for hyperspectral anomaly
detection, " IEEE Trans. Cybern., vol. 53, no. 1, pp. 679-691, Jan.
2023, doi: 10.1109/TCYB.2022.3175771.
[78] C. I. Kanatsoulis, X. Fu, N. D. Sidiropoulos, and W.-K. Ma, " Hyperspectral
super-resolution: A coupled tensor factorization
approach, " IEEE Trans. Signal Process., vol. 66, no. 24, pp. 6503-
6517, Dec. 2018, doi: 10.1109/TSP.2018.2876362.
[79] Y. Xu, Z. Wu, J. Chanussot, P. Comon, and Z. Wei, " Nonlocal coupled
tensor CP decomposition for hyperspectral and multispectral
image fusion, " IEEE Trans. Geosci. Remote Sens., vol. 58, no. 1,
pp. 348-362, Jan. 2020, doi: 10.1109/TGRS.2019.2936486.
[80] C. I. Kanatsoulis, X. Fu, N. D. Sidiropoulos, and W.-K. Ma, " Hyperspectral
super-resolution: Combining low rank tensor and
matrix structure, " in Proc. IEEE Int. Conf. Image Process., 2018,
pp. 3318-3322, doi: 10.1109/ICIP.2018.8451733.
[81] S. Li, R. Dian, L. Fang, and J. M. Bioucas-Dias, " Fusing
hyperspectral and multispectral
images via coupled
66
sparse tensor factorization, " IEEE Trans. Image Process.,
vol. 27, no. 8, pp. 4118-4130, Aug. 2018, doi: 10.1109/
TIP.2018.2836307.
[82] R. A. Borsoi, C. Prévost, K. Usevich, D. Brie, J. C. M. Bermudez,
and C. Richard, " Coupled tensor decomposition for hyperspectral
and multispectral image fusion with inter-image variability, "
IEEE J. Sel. Topics Signal Process., vol. 15, no. 3, pp. 702-717,
Apr. 2021, doi: 10.1109/JSTSP.2021.3054338.
[83] M. Zare, M. S. Helfroush, K. Kazemi, and P. Scheunders, " Hyperspectral
and multispectral image fusion using coupled nonnegative
tucker tensor decomposition, " Remote Sens., vol. 13,
no. 15, 2021, Art. no. 2930, doi: 10.3390/rs13152930.
[84] H. Li, W. Li, G. Han, and F. Liu, " Coupled tensor decomposition
for hyperspectral pansharpening, " IEEE
Access,
vol. 6, pp. 34,206-34,213, Jun. 2018, doi: 10.1109/ACCESS.2018.2850340.
[85]
C. Prévost, K. Usevich, P. Comon, and D. Brie, " Hyperspectral
super-resolution with coupled tucker approximation:
Recoverability and SVD-based algorithms, " IEEE Trans. Signal
Process., vol. 68, pp. 931-946, Jan. 2020, doi: 10.1109/
TSP.2020.2965305.
[86] W. Wan, W. Guo, H. Huang, and J. Liu, " Nonnegative and nonlocal
sparse tensor factorization-based hyperspectral image super-resolution, "
IEEE Trans. Geosci. Remote Sens., vol. 58, no. 12,
pp. 8384-8394, Dec. 2020, doi: 10.1109/TGRS.2020.2987530.
[87] R. Dian, L. Fang, and S. Li, " Hyperspectral image super-resolution
via non-local sparse tensor factorization, " in Proc. IEEE
Conf. Comput. Vis. Pattern Recognit., 2017, pp. 3862-3871.
[88] R. Dian, S. Li, L. Fang, T. Lu, and J. M. Bioucas-Dias, " Nonlocal
sparse tensor factorization for semiblind hyperspectral and
multispectral image fusion, " IEEE Trans. Cybern., vol. 50, no. 10,
pp. 4469-4480, 2020, doi: 10.1109/TCYB.2019.2951572.
[89] T. Xu, T.-Z. Huang, L.-J. Deng, X.-L. Zhao, and J. Huang, " Hyperspectral
image superresolution using unidirectional total
variation with tucker decomposition, " IEEE J. Sel. Topics Appl.
Earth Observ. Remote Sens., vol. 13, pp. 4381-4398, Jul. 2020,
doi: 10.1109/JSTARS.2020.3012566.
[90] K. Wang, Y. Wang, X.-L. Zhao, J. C.-W. Chan, Z. Xu, and D.
Meng, " Hyperspectral and multispectral
image fusion via
nonlocal low-rank tensor decomposition and spectral unmixing, "
IEEE Trans. Geosci. Remote Sens., vol. 58, no. 11, pp. 7654-
7671, Nov. 2020, doi: 10.1109/TGRS.2020.2983063.
[91] K. Zhang, M. Wang, S. Yang, and L. Jiao, " Spatial-spectralgraph-regularized
low-rank tensor decomposition for multispectral
and hyperspectral image fusion, " IEEE J. Sel. Topics
Appl. Earth Observ. Remote Sens., vol. 11, no. 4, pp. 1030-1040,
Apr. 2018, doi: 10.1109/JSTARS.2017.2785411.
[92] Y. Bu et al., " Hyperspectral and multispectral image fusion via
graph Laplacian-guided coupled tensor decomposition, " IEEE
Trans. Geosci. Remote Sens., vol. 59, no. 1, pp. 648-662, Jan.
2021, doi: 10.1109/TGRS.2020.2992788.
[93] G. Zhang, X. Fu, K. Huang, and J. Wang, " Hyperspectral superresolution:
A coupled nonnegative block-term tensor decomposition
approach, " in Proc. IEEE Int. Workshop Comput. Adaptive
Multi-Sens. Adapt. Process., 2019, pp. 470-474, doi: 10.1109/
CAMSAP45676.2019.9022476.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MARCH 2023
IEEE Geoscience and Remote Sensing Magazine - March 2023

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