Semiconductor Optoelectronics, Volume. 45, Issue 6, 925(2024)

Hyperspectral Image Unmixing Algorithm Based on Autoencoder and Multi-scale Spatial-Spectral Feature Encoding

ZHANG Chengbi, YANG Huadong, LI Shihui, and CHEN Liyuan
Author Affiliations
  • School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, CHN
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    References(14)

    [2] [2] Keshava N, Mustard J F. Spectral unmixing [J]. IEEE Signal Processing Magazine, 2002, 19(1): 44-57.

    [4] [4] Boardman J W, Kruse F A, Green R O. Mapping target signatures via partial unmixing of AVIRIS data [C]// Summaries of the Fifth Annual JPL Airborne Earth Science Workshop, 1995: 127556070.

    [5] [5] Winter M E. N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data [J]. Proc. SPIE, 1999, 3753: 266-275.

    [6] [6] Nascimento J M P, Dias J M B. Vertex component analysis: a fast algorithm to unmix hyperspectral data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.

    [7] [7] Palsson B, Sveinsson J R, Ulfarsson M O. Blind hyperspectral unmixing using autoencoders: A critical comparison [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 1340-1372.

    [8] [8] Su Y, Marinoni A, Li J, et al. Stacked nonnegative sparse autoencoders for robust hyperspectral unmixing [J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(9): 1427-1431.

    [9] [9] Palsson B, Sigurdsson J, Sveinsson J R, et al. Hyperspectral unmixing using a neural network autoencoder [J]. IEEE Access, 2018, 6: 25646-25656.

    [10] [10] Palsson B, Ulfarsson M O, Sveinsson J R. Convolutional autoencoder for spatial-spectral hyperspectral unmixing [C]// IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019: 357-360.

    [11] [11] Gao L, Han Z, Hong D, et al. CyCU-Net: Cycle-consistency unmixing network by learning cascaded autoencoders [J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-14.

    [12] [12] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [J]. Advances in Neural Information Processing Systems, 2017, 30: 6000-6010.

    [13] [13] Ghosh P, Roy S K, Koirala B, et al. Hyperspectral unmixing using transformer network [J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-16.

    [14] [14] Yang Z, Xu M, Liu S, et al. UST-Net: A U-shaped transformer network using shifted windows for hyperspectral unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-15.

    [15] [15] Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: Transformers for image recognition at scale [J]. arXiv preprint arXiv: 2010.11929, 2020.

    [16] [16] Liu Z, Lin Y, Cao Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows [C]// Proc. of the IEEE/CVF International Conf. on Computer Vision, 2021: 10012-10022.

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    ZHANG Chengbi, YANG Huadong, LI Shihui, CHEN Liyuan. Hyperspectral Image Unmixing Algorithm Based on Autoencoder and Multi-scale Spatial-Spectral Feature Encoding[J]. Semiconductor Optoelectronics, 2024, 45(6): 925

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    Paper Information

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    Received: May. 26, 2024

    Accepted: Feb. 28, 2025

    Published Online: Feb. 28, 2025

    The Author Email:

    DOI:10.16818/j.issn1001-5868.2024052601

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