Journal of Infrared and Millimeter Waves, Volume. 42, Issue 6, 824(2023)

Research on hyperspectral image classification method based on deep learning

Bin ZHANG1, Liang LIU2, Xiao-Jie LI1, and Wei ZHOU1、*
Author Affiliations
  • 1Aviation Operations and Service Institute,Naval Aviation University,Yantai 264000,China
  • 2Coastal Defense College,Naval Aviation University,Yantai 264000,China
  • show less
    References(27)

    [1] Cen Y, Zhang L F, Zhang X et al. Aerial hyperspectral remote sensing classification dataset of Xiongan New Area (Matiwan Village)[J]. Journal of Remote Sensing (Chinese), 24, 1299-1306(2020).

    [2] Zhang F, Zhang J S, Pei C X. A classification algorithm of hyperspectral images based on double channel temporal dense network[J]. Journal of Xian Jiao tong University, 54, 126-132(2020).

    [3] Gao K L, Yu X C, Zhang P Q et al. Hyperspectral image spatial-spectral classification using capsule network-based method[J]. Geomatics and Information Science of Wuhan University, 47, 428-437(2022).

    [4] Xu Y H. Research on deep learning and adversarial defense methods for hyperspectral remote sensing image classification[J]. Geomatics and Information Science of Wuhan University, 47, 157(2022).

    [5] Xie H, Tong X H. A probability-based improved binary encoding algorithm for classification of hyperspectral images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2108-2118(2014).

    [6] Prasad S, Bruce L M. Limitations of principal components analysis for hyperspectral target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 5, 625-629.

    [7] Villa A, Benediktsson J A, Chanussot J et al. Hyperspectral image classification with independent component discriminant analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 49, 4865-4876(2011).

    [8] Bandos T V, Bruzzone L, Camps-Valls G. Classification of hyperspectral images with regularized linear discriminant analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 47, 862-873(2009).

    [9] Blanzieri E, Melgani F. Nearest neighbor classification of remote sensing images with the maximal margin principle[J]. IEEE Transactions on Geoscience and Remote Sensing, 46, 1804-1811(2008).

    [10] Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 42, 1778-1790(2004).

    [11] Gislason P O, Benediktsson J A, Sveinsson J R. Random forests for land cover classification[J]. Pattern Recognition Letters, 27, 294-300(2006).

    [12] Pal M. Extreme-learning-machine-based land cover classification[J]. International Journal of Remote Sensing, 30, 3835-3841(2009).

    [13] Friedl M A, Brodley C E. Decision tree classification of land cover from remotely sensed data[J]. Remote Sensing of Environment, 61, 399-409(1997).

    [14] Chen Y, Jiang H L, Li C Y et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 6232-6251(2016).

    [15] Li W, Wu G D, Zhang F et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 844-853(2017).

    [16] Xie J, He N J, Fang L Y et al. Multiscale densely-connected fusion networks for hyperspectral images classification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 31, 246-259(2021).

    [17] Zheng X T, Sun H, Lu X Q et al. Rotation-invariant attention network for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 31, 4251-4265(2022).

    [18] Haut J, Paoletti M, Paz-Gallardo A et al. Cloud implementation of logistic regression for hyperspectral image classification. 17th International Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE)(2017).

    [19] Ham J, Chen Y, Crawford M M et al. Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43, 492-501(2005).

    [20] Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, 1778-1790(2004).

    [21] Paoletti M E, Haut J M, Plaza J et al. Scalable recurrent neural network for hyperspectral image classification. Journal of Supercomputing, 76, 8866-8882(2020).

    [22] He K, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016).

    [23] Lee H, Kwon H. Going deeper with contextual CNN for hyperspectral image classification. IEEE Transactions on Image Processing, 26, 4843-4855(2017).

    [24] He M Y, Li B, Chen H H. Multi-scale 3D deep convolutional neural network for hyperspectral image classification(Sep.2017).

    [25] Shen Y, Zhu S J, Chen C et al. Efficient deep learning of nonlocal features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 59, 6029-6043(2020).

    [26] Paoletti M E, Haut J M, Fernandez-Beltran R et al. Deep pyramidal residual networks for spectral-spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57, 740-754(2019).

    [27] Zhong Z L, Li J, Luo Z M et al. Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework. IEEE Transactions on Geoscience and Remote Sensing, 56, 847-858(2018).

    Tools

    Get Citation

    Copy Citation Text

    Bin ZHANG, Liang LIU, Xiao-Jie LI, Wei ZHOU. Research on hyperspectral image classification method based on deep learning[J]. Journal of Infrared and Millimeter Waves, 2023, 42(6): 824

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Research Articles

    Received: Jan. 6, 2023

    Accepted: --

    Published Online: Dec. 26, 2023

    The Author Email: Wei ZHOU (yeaweam@163.com)

    DOI:10.11972/j.issn.1001-9014.2023.06.016

    Topics