Fusion of ConvLSTM and Multi-Attention Mechanism Network for Hyperspectral Image Classification

TANG Ting; PAN Xin; LUO Xiao-ling; GAO Xiao-jing

doi:10.3964/j.issn.1000-0593(2023)08-2608-09

[2] Melgani F, Bruzzone L. Transactions on Geoscience and Remote Sensing, 2004, 42: 1778.

[3] Li H C, Zhou H L, Pan L, et al. Electronics Letters, 2018, 54(10): 628.

[4] Yu S Q, Jia S, Xu C Y. Neurocomputing, 2017, 219: 88.

[5] Chen Y S, Jiang H L, Li C Y, et al. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6232.

[6] Lee H, Kwon H. IEEE Transactions on Image Processing, 2017, 26: 4843.

[7] He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recoynition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, 770.

[8] Huang G, Liu Z, Van Der Maaten L, et al. Densely Connected Convolutional Networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 2261.

[9] Zhong Z L, Li J, Luo Z M, et al. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(2): 847.

[10] Wang W, Dou S, Jiang Z, et al. Remote Sensing, 2018, 10: 1068.

[11] Ma W, Yang Q, Wu Y, et al. Remote Sensing, 2019, 11: 1307.

[12] Li R, Zheng S, Duan C, et al. Remote Sensing, 2020, 12(3): 582.

[13] Lecon D, Gaetano R, Dupaquier C, et al. IEEE Geoscience and Remote Sensing Letters, Lett, 2017, 14: 1685.

[14] Shi X J, Chen Z R, Wang H, et al. Convolutional LSTM Network: A Machine Learing Approach for Precipitation Nowcasting, 2015, arXiv: 1506. 04214［CS.CV］.

[16] Mnih V, Heess N, Graves A, et al. Recurrent Models of Visual Attention, 2014, arXiv: 1406. 6247v1［CS.LG］.