• Laser & Optoelectronics Progress
  • Vol. 60, Issue 12, 1228002 (2023)
Yuhan Chen, Bo Wang*, Qingyun Yan, Bingjie Huang, Tong Jia, and Bin Xue
Author Affiliations
  • School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
  • show less
    DOI: 10.3788/LOP220921 Cite this Article Set citation alerts
    Yuhan Chen, Bo Wang, Qingyun Yan, Bingjie Huang, Tong Jia, Bin Xue. Hyperspectral Remote-Sensing Classification Combining Transformer and Multiscale Residual Mechanisms[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228002 Copy Citation Text show less
    References

    [1] Sun W W, Liu K, Ren G B et al. A simple and effective spectral-spatial method for mapping large-scale coastal wetlands using China ZY1-02D satellite hyperspectral images[J]. International Journal of Applied Earth Observation and Geoinformation, 104, 102572(2021).

    [2] Stuart M B, McGonigle A J S, Willmott J R. Hyperspectral imaging in environmental monitoring: a review of recent developments and technological advances in compact field deployable systems[J]. Sensors, 19, 3071(2019).

    [3] Su H J, Yao W J, Wu Z Y et al. Kernel low-rank representation with elastic net for China coastal wetland land cover classification using GF-5 hyperspectral imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 238-252(2021).

    [4] Moughal T A. Hyperspectral image classification using Support Vector Machine[J]. Journal of Physics: Conference Series, 439, 012042(2013).

    [5] Petropoulos G P, Arvanitis K, Sigrimis N. Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping[J]. Expert Systems With Applications, 39, 3800-3809(2012).

    [6] Golhani K, Balasundram S K, Vadamalai G et al. A review of neural networks in plant disease detection using hyperspectral data[J]. Information Processing in Agriculture, 5, 354-371(2018).

    [7] Chen Y S, Zhao X, Jia X P. Spectral-spatial classification of hyperspectral data based on deep belief network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2381-2392(2015).

    [8] Hong D F, Gao L R, Yao J et al. Graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 5966-5978(2021).

    [9] Feng F, Wang S T, Zhang J et al. Hyperspectral images classification based on multi-feature fusion and hybrid convolutional neural networks[J]. Laser & Optoelectronics Progress, 58, 0810010(2021).

    [10] Hommel B, Chapman C S, Cisek P et al. No one knows what attention is[J]. Attention, Perception, & Psychophysics, 81, 2288-2303(2019).

    [11] Wang X, Fan Y G. Hyperspectral image classification based on modified DenseNet and spatial spectrum attention mechanism[J]. Laser & Optoelectronics Progress, 59, 0210014(2022).

    [12] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https://arxiv.org/abs/1706.03762

    [13] Qing Y H, Liu W Y, Feng L Y et al. Improved transformer net for hyperspectral image classification[J]. Remote Sensing, 13, 2216(2021).

    [14] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth16x16 words: transformers for image recognition at scale[EB/OL]. https://arxiv.org/abs/2010.11929

    [15] Liu Z, Lin Y T, Cao Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2021).

    [16] Cao J Y, Chen Z, Wang B. Deep Convolutional networks with superpixel segmentation for hyperspectral image classification[C], 3310-3313(2016).

    [17] Liu Y G, Yu J Z, Han Y H. Understanding the effective receptive field in semantic image segmentation[J]. Multimedia Tools and Applications, 77, 22159-22171(2018).

    [18] Garbin C, Zhu X Q, Marques O. Dropout vs. batch normalization: an empirical study of their impact to deep learning[J]. Multimedia Tools and Applications, 79, 12777-12815(2020).

    [19] Sun W W, Shao W J, Peng J T et al. Multiscale low-rank spatial features for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 19, 5501605(2022).

    [20] Raghu M, Unterthiner T, Kornblith S et al. Do vision transformers see like convolutional neural networks?[EB/OL]. https://arxiv.org/abs/2108.08810

    [21] Hu W, Huang Y Y, Wei L et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 258619(2015).

    [22] Hamida A B, Benoit A, Lambert P et al. 3-D deep learning approach for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 4420-4434(2018).

    [23] Liao J L, Zhang L, Zhou X S et al. A hyperspectral image vegetation classification method using 2D-3D CNNs and vegetation index[J]. Science Technology and Engineering, 21, 11656-11662(2021).

    [24] Xu Q, Liang Y L, Wang D Y et al. Hyperspectral image classification based on SE-Res2Net and multi-scale spatial spectral fusion attention mechanism[J]. Journal of Computer-Aided Design & Computer Graphics, 33, 1726-1734(2021).

    [25] Touvron H, Vedaldi A, Douze M et al. Fixing the train-test resolution discrepancy: FixEfficientNet[EB/OL]. https://arxiv.org/abs/2003. 08237

    [26] Ji S W, Xu W, Yang M et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 221-231(2013).

    [27] Zhou B L, Khosla A, Lapedriza A et al. Learning deep features for discriminative localization[C], 2921-2929(2016).

    Yuhan Chen, Bo Wang, Qingyun Yan, Bingjie Huang, Tong Jia, Bin Xue. Hyperspectral Remote-Sensing Classification Combining Transformer and Multiscale Residual Mechanisms[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228002
    Download Citation