A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 9 Issue 10
Oct.  2022

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(10): 1892-1895
L. Y. Fang, D. S. Zhu, J. Yue, B. Zhang, and M. He, “Geometric-spectral reconstruction learning for multi-source open-set classification with hyperspectral and LiDAR data,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1892–1895, Oct. 2022. doi: 10.1109/JAS.2022.105893
Citation: L. Y. Fang, D. S. Zhu, J. Yue, B. Zhang, and M. He, “Geometric-spectral reconstruction learning for multi-source open-set classification with hyperspectral and LiDAR data,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1892–1895, Oct. 2022. doi: 10.1109/JAS.2022.105893
shu

Geometric-Spectral Reconstruction Learning for Multi-Source Open-Set Classification With Hyperspectral and LiDAR Data

doi: 10.1109/JAS.2022.105893
  • 1.

    College of Electrical and Information Engineering, Hunan University, Changsha 410082, and also with the Peng Cheng Laboratory, Shenzhen 518000, China

  • 2.

    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

  • 3.

    Department of Geomatics Engineering, Changsha University of Science and Technology, Changsha 410114, China

  • 4.

    PAMI Research Group, the Department of Computer and Information Science, University of Macau, Macau 999078, China

More Information
    Abstract
    • FullText(HTML)
  • loading
    • References(20)
  • [1]
    A. Bendale and T. E. Boult, “Towards open set deep networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2016, pp. 1563–1572.
    [2]
    W. J. Scheirer, A. de Rezende Rocha, A. Sapkota, and T. E. Boult, “Toward open set recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 35, no. 7, pp. 1757–1772, Jul. 2013. doi: 10.1109/TPAMI.2012.256
    [3]
    P. Oza and V. M. Patel, “C2AE: Class conditioned auto-encoder for open-set recognition,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2019, pp. 2307–2316.
    [4]
    C. Geng, S.-J. Huang, and S. Chen, “Recent advances in open set recognition: A survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 43, no. 10, pp. 3614–3631, Oct. 2021. doi: 10.1109/TPAMI.2020.2981604
    [5]
    Y. Liu, Y. H. Tang, L. X. Zhang, L, Liu, M. H. Song, et al., “Hyperspectral open set classification with unknown classes rejection towards deep networks,” Int. J. Remote Sensing, vol. 41, no. 16, pp. 6355–6383, 2020. doi: 10.1080/01431161.2020.1754492
    [6]
    R. Yoshihashi, W. Shao, R. Kawakami, S. You, M. Iida, and T. Naemura, “Classification-reconstruction learning for open-set recognition,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2019, pp. 4016–4025.
    [7]
    W. J. Scheirer, L. P. Jain, and T. E. Boult, “Probability models for open set recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 36, no. 11, pp. 2317–2324, Nov. 2014.
    [8]
    L. Neal, M. Olson, X. Fern, W.-K. Wong, and F. Li, “Open set learning with counterfactual images,” in Proc. European Conf. Computer Vision, 2018, pp. 613–628.
    [9]
    J. Yue, D. Zhu, L. Fang, P. Ghamisi, and Y. Wang, “Adaptive spatial pyramid constraint for hyperspectral image classification with limited training samples,” IEEE Trans. Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2021. doi: 10.1109/TGRS.2021.3095056
    [10]
    Z. Zhong, J. Li, Z. Luo, and M. Chapman, “Spectral–Spatial residual network for hyperspectral image classification: A 3D deep learning framework,” IEEE Trans. Geoscience and Remote Sensing, vol. 56, no. 2, pp. 847–858, Feb. 2018.
    [11]
    S. Xia, D. Chen, R. Wang, J. Li, and X. Zhang, “Geometric primitives in LiDAR point clouds: A review,” IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 685–707, 2020. doi: 10.1109/JSTARS.2020.2969119
    [12]
    J. Yue, W. Zhao, S. Mao, and H. Liu, “Spectral-spatial classification of hyperspectral images using deep convolutional neural networks,” Remote Sensing Letters, vol. 6, no. 6, pp. 468–477, 2015. doi: 10.1080/2150704X.2015.1047045
    [13]
    J. Kang, R. Fernandez-Beltran, Z. Wang, X. Sun, J. Ni, and A. Plaza, “Rotation-invariant deep embedding for remote sensing images,” IEEE Trans. Geoscience and Remote Sensing, vol. 60, pp. 1–13, 2022.
    [14]
    J. Kang, Z. Wang, R. Zhu, X. Sun, R. Fernandez-Beltran, and A. Plaza, “PiCoCo: Pixelwise contrast and consistency learning for semisupervised building footprint segmentation,” IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 10548–10559, 2021. doi: 10.1109/JSTARS.2021.3119286
    [15]
    H. Liu, M. Zhou, and Q. Liu, “An embedded feature selection method for imbalanced data classification,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 703–715, May 2019. doi: 10.1109/JAS.2019.1911447
    [16]
    S. Liu, Q. Shi, and L. Zhang, “Few-shot hyperspectral image classification with unknown classes using multitask deep learning,” IEEE Trans. Geoscience and Remote Sensing, vol. 59, no. 6, pp. 5085–5102, 2021. doi: 10.1109/TGRS.2020.3018879
    [17]
    W. J. Scheirer, A. Rocha, R. J. Micheals, and T. E. Boult, “Meta-recognition: The theory and practice of recognition score analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1689–1695, Aug. 2011. doi: 10.1109/TPAMI.2011.54
    [18]
    L. Mou, P. Ghamisi, and X. X. Zhu, “Deep recurrent neural networks for hyperspectral image classification,” IEEE Trans. Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3639–3655, 2017. doi: 10.1109/TGRS.2016.2636241
    [19]
    J. Yue, L. Fang, H. Rahmani, and P. Ghamisi, “Self-supervised learning with adaptive distillation for hyperspectral image classification,” IEEE Trans. Geoscience and Remote Sensing, vol. 60, pp. 1–13, 2022. doi: 10.1109/TGRS.2021.3057768
    [20]
    J. Wang, J. Zhou, X. Liu, and F. Jahan, “Spectral and spatial residual attention network for joint hyperspectral and LIDAR data classification,” in Proc IEEE Int. Geoscience and Remote Sensing Symposium, Jul. 2021, pp. 278–281.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(1)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (561) PDF downloads(69) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return