IEEE Geoscience and Remote Sensing Magazine - June 2022 - 193

[54] V. S. F. Garnot, L. Landrieu, S. Giordano, and N. Chehata, " Satellite
image time series classification with pixel-set encoders
and temporal self-attention, " 2019, arXiv:1911.07757.
[55] M. O. Turkoglu, S. D'Aronco, J. D. Wegner, and K. Schindler,
" Gating revisited: Deep multi-layer RNNs that can be trained, "
IEEE Trans. Pattern Anal. Mach. Intell., early access, 2021, doi:
10.1109/TPAMI.2021.3064878.
[56] S. Hochreiter and J. Schmidhuber, " Long short-term memory, "
Neural Comput., vol. 9, no. 8, pp. 1735-1780, 1997, doi: 10.1162/
neco.1997.9.8.1735.
[57] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, " Empirical evaluation
of gated recurrent neural networks on sequence modeling, "
in Proc. NIPS Workshop, 2014.
[58] Y. Bengio, " Learning deep architectures for AI, " Found. Trends
Mach. Learning, vol. 2, no. 1, pp. 1-127, 2009, doi: 10.1561/
2200000006.
[59] K. He, G. Gkioxari, P. Dollár, and R. Girshick, " Mask R-CNN, "
in Proc. IEEE Int. Conf. Comput. Vision (ICCV), 2017, pp. 2980-
2988, doi: 10.1109/ICCV.2017.322.
[60] T. Hank et al., " Spaceborne imaging spectroscopy for sustainable
agriculture: Contributions and challenges, " Surveys Geophys.,
vol. 40, no. 3, pp. 515-551, 2018, doi: 10.1007/s10712018-9492-0.
[61]
A. I. Flores-Anderson et al., " Hyperspectral satellite remote
sensing of water quality in Lake Atitlan, Guatemala, " Front. Environ.
Sci., vol. 8, p. 7, 2020, doi: 10.3389/fenvs.2020.00007.
[62] J. P. Ryan, C. O. Davis, N. B. Tufillaro, R. M. Kudela, and B.-C.
Gao, " Application of the hyperspectral imager for the coastal
ocean to phytoplankton ecology studies in Monterey Bay, CA,
USA, " Remote Sens., vol. 6, no. 2, pp. 1007-1025, 2014, doi:
10.3390/rs6021007.
[63] M. E. Fagan et al., " Mapping species composition of forests
and tree plantations in northeastern Costa Rica with an integration
of hyperspectral and multitemporal Landsat imagery, "
Remote Sens., vol. 7, no. 5, pp. 5660-5696, 2015, doi: 10.3390/
rs70505660.
[64] P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. J. Plaza, " Advanced
spectral classifiers for hyperspectral images: A review, " IEEE
Geosci. Remote Sens. Mag. (replaces Newslett.), vol. 5, no. 1,
pp. 8-32, 2017, doi: 10.1109/MGRS.2016.2616418.
[65] W. Zhao and S. Du, " Spectral-spatial feature extraction
for hyperspectral image classification: A dimension reduction
and deep learning approach, " IEEE Trans. Geosci. Remote
Sens., vol. 54, no. 8, pp. 4544-4554, 2016, doi: 10.1109/TGRS.
2016.2543748.
[66] Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, " Deep feature
extraction and classification of hyperspectral images based
on convolutional neural networks, " IEEE Trans. Geosci. Remote
Sens., vol. 54, no. 10, pp. 6232-6251, 2016, doi: 10.1109/
TGRS.2016.2584107.
[67] W. Song, S. Li, L. Fang, and T. Lu, " Hyperspectral image classification
with deep feature fusion network, " IEEE Trans. Geosci.
Remote Sens., vol. 56, no. 6, pp. 3173-3184, 2018, doi: 10.1109/
TGRS.2018.2794326.
[68] Y. Kong, X. Wang, and Y. Cheng, " Spectral-spatial feature extraction
for HSI classification based on supervised hypergraph
JUNE 2022 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
and sample expanded CNN, " IEEE J. Sel. Topics Appl. Earth Observ.
Remote Sens., vol. 11, no. 11, pp. 4128-4140, Nov. 2018,
doi: 10.1109/JSTARS.2018.2869210.
[69] X. He, Y. Chen, and P. Ghamisi, " Heterogeneous transfer learning
for hyperspectral image classification based on convolutional
neural network, " IEEE Trans. Geosci. Remote Sens., vol. 58,
no. 5, pp. 3246-3263, 2020, doi: 10.1109/TGRS.2019.2951445.
[70] L. Mou, P. Ghamisi, and X. X. Zhu, " Unsupervised spectral-
spatial feature learning via deep residual conv-deconv network
for hyperspectral
image classification, " IEEE Trans. Geosci.
Remote Sens., vol. 56, no. 1, pp. 391-406, 2018, doi: 10.1109/
TGRS.2017.2748160.
[71] F. Zhou, R. Hang, Q. Liu, and X. Yuan, " Hyperspectral image
classification using spectral-spatial LSTMS, " Neurocomputing, vol.
328, pp. 39-47, Feb. 2019, doi: 10.1016/j.neucom.2018.02.105.
[72] Q. Liu, F. Zhou, R. Hang, and X. Yuan, " Bidirectional-convolutional
LSTM based spectral-spatial feature learning for hyperspectral
image classification, " Remote Sens., vol. 9, no. 12, p.
1330, 2017, doi: 10.3390/rs9121330.
[73] R. Hang, Q. Liu, D. Hong, and P. Ghamisi, " Cascaded recurrent
neural networks for hyperspectral image classification, " IEEE
Trans. Geosci. Remote Sens., vol. 57, no. 8, pp. 5384-5394, 2019,
doi: 10.1109/TGRS.2019.2899129.
[74] Y. Xu, L. Zhang, B. Du, and F. Zhang, " Spectral-spatial unified
networks for hyperspectral image classification, " IEEE Trans.
Geosci. Remote Sens., vol. 56, no. 10, pp. 5893-5909, 2018, doi:
10.1109/TGRS.2018.2827407.
[75] L. Mou, P. Ghamisi, and X. X. Zhu, " Deep recurrent neural
networks for hyperspectral image classification, " IEEE Trans.
Geosci. Remote Sens., vol. 55, no. 7, pp. 3639-3655, 2017, doi:
10.1109/TGRS.2016.2636241.
[76] X. Zhu et al., " Deep learning meets SAR: Concepts, models,
pitfalls, and perspectives, " IEEE Geosci. Remote Sens. Mag.
(replaces Newslett.), early access, 2021, doi: 10.1109/MGRS.
2020.3046356.
[77] J. Geng, J. Fan, H. Wang, X. Ma, B. Li, and F. Chen, " High-resolution
SAR image classification via deep convolutional autoencoders, "
IEEE Geosci. Remote Sens. Lett., vol. 12, no. 11, pp.
2351-2355, 2015, doi: 10.1109/LGRS.2015.2478256.
[78] Z. Zhang, H. Wang, F. Xu, and Y. Jin, " Complex-valued convolutional
neural network and its application in polarimetric
SAR image classification, " IEEE Trans. Geosci. Remote
Sens., vol. 55, no. 12, pp. 7177-7188, 2017, doi: 10.1109/
TGRS.2017.2743222.
[79] A. G. Mullissa, C. Persello, and A. Stein, " PolSARNET: A deep fully
convolutional network for polarimetric SAR image classification, "
IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 12, no.
12, pp. 5300-5309, 2019, doi: 10.1109/JSTARS.2019.2956650.
[80] H. Dong, B. Zou, L. Zhang, and S. Zhang, " Automatic design of
CNNs via differentiable neural architecture search for polSAR
image classification, " IEEE Trans. Geosci. Remote Sens., vol. 58,
no. 9, pp. 6362-6375, 2020, doi: 10.1109/TGRS.2020.2976694.
[81] Z. Huang, M. Datcu, Z. Pan, and B. Lei, " Deep SAR-NET:
Learning objects from signals, " ISPRS J. Photogrammetry Remote
Sens., vol. 161, pp. 179-193, Jan. 2020, doi: 10.1016/j.isprsjprs.2020.01.016.
193
IEEE Geoscience and Remote Sensing Magazine - June 2022

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2022
