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Volume 32Issue 1
Feb. 2023
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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2023> 32(1): 13-22
Yongqiang Xi, Zhen Ye. Multi-Scale Feature Extraction for Joint Classification of Hyperspectral and LiDAR Data[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2023, 32(1): 13-22. doi: 10.15918/j.jbit1004-0579.2022.120
Citation: Yongqiang Xi, Zhen Ye. Multi-Scale Feature Extraction for Joint Classification of Hyperspectral and LiDAR Data[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2023, 32(1): 13-22.doi:10.15918/j.jbit1004-0579.2022.120
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Multi-Scale Feature Extraction for Joint Classification of Hyperspectral and LiDAR Data

doi:10.15918/j.jbit1004-0579.2022.120

  • School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China

Funds:The associate editor coordinating the review of this manuscript was Dr. Na Liu. This work was supported by the National Key Research and Development Project (No. 2020YFC1512000)，the General Projects of Key R&D Programs in Shaanxi Province (No. 2020GY-060) and Xi’an Science & Technology Project (No. 2020KJRC0126)
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  • Author Bio:

    Yongqiang Xireceived the B.E. degree from the School of Electronics and Control Engineering, Chang’an University, Xi’an, China, in 2020. He is currently pursuing an M.E. degree in the School of Electronics and Control Engineering, Chang’an University, Xi’an, China. His current research interests is multi-source remote sensing image fusion and classification

    Zhen Yereceived the B.S., M.S., and Ph.D. degrees in information and communication engineering from Northwestern Polytechnical University, Xi’an, China, in 2007, 2010, and 2015, respectively. She spent one year as an Exchange Student with Mississippi State University, Mississippi State, MS, USA. She is currently an Associate Professor with the School of Electronics and Control Engineering, Chang’an University, Xi’an, China. Her research interests include hyperspectral image analysis, pattern recognition, and machine learning

  • Corresponding author:yezhen525@126.com
  • Received Date:2022-10-28
  • Rev Recd Date:2022-12-01
  • Accepted Date:2022-12-04
  • Publish Date:2023-02-28
    Abstract
  • With the development of sensors, the application of multi-source remote sensing data has been widely concerned. Since hyperspectral image (HSI) contains rich spectral information while light detection and ranging (LiDAR) data contains elevation information, joint use of them for ground object classification can yield positive results, especially by building deep networks. Fortunately, multi-scale deep networks allow to expand the receptive fields of convolution without causing the computational and training problems associated with simply adding more network layers. In this work, a multi-scale feature fusion network is proposed for the joint classification of HSI and LiDAR data. First, we design a multi-scale spatial feature extraction module with cross-channel connections, by which spatial information of HSI data and elevation information of LiDAR data are extracted and fused. In addition, a multi-scale spectral feature extraction module is employed to extract the multi-scale spectral features of HSI data. Finally, joint multi-scale features are obtained by weighting and concatenation operations and then fed into the classifier. To verify the effectiveness of the proposed network, experiments are carried out on the MUUFL Gulfport and Trento datasets. The experimental results demonstrate that the classification performance of the proposed method is superior to that of other state-of-the-art methods.
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    • References (23)
  • [1]
    J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles, ” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 3, pp. 480-491, 2005.
    [2]
    J. Zhang, X. Lin, and X. Ning, “SVM-based classification of segmented airborne LiDAR point clouds in urban areas, ” Remote Sensing, vol. 5, no. 8, pp. 3749-3775, 2013. .
    [3]
    Y. F. Zhong, Q. Cao, J. Zhao, A. L. Ma, B. Zhao, and L. P. Zhang, “Optimal decision fusion for urban land-use/land-cover classification based on adaptive differential evolution using hyperspectral and LiDAR data, ” Remote Sensing, vol. 9, no. 8, pp. 868, 2017.
    [4]
    A. Merentitis, C. Debes, R. Heremans, and N. Frangiadakis, “Automatic fusion and classification of hyperspectral and LiDAR data using random forests, ” in 2014 IEEE Geoscience and Remote Sensing Symposium(IGARSS), pp. 1245-1248, 2014.
    [5]
    B. Rasti, P. Ghamisi, J. Plaza, and A. Plaza, “Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis, ” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 11, pp. 6354-6365, 2017.
    [6]
    M. Zhang, P. Ghamisi, and W. Li, “Classification of hyperspectral and LiDAR data using extinction profiles with feature fusion, ” Remote Sensing Letters, vol. 8, no. 10, pp. 957-966, 2017.
    [7]
    Y. Chen, C. Li, P. Ghamisi, X. Jia, and Y. Gu, “Deep fusion of remote sensing data for accurate classification, ” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1253-1257, 2017.
    [8]
    Y. Chen, C. Li, P. Ghamisi, X. Jia, and Y. Gu, “Deep fusion of hyperspectral and LiDAR data for thematic classification, ” in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3591-3594, 2016.
    [9]
    T. Zhang, S. Xiao, W. Dong, J. Qu, and Y. Yang, “A mutual guidance attention-based multi-level fusion network for hyperspectral and LiDAR classification, ” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2021.
    [10]
    M. Zhang, W. Li, Q. Du, L. Gao, and B. Zhang, “Feature extraction for classification of hyperspectral and LiDAR data using patch-to-patch CNN, ” IEEE Transactions on Cybernetics, vol. 50, no. 1, pp. 100-111, 2018.
    [11]
    S. Jia, Z. Zhan, M. Zhang, M. Xu, Q. Huang, J. Zhou, and X. Jia, “Multiple feature-based superpixel-level decision fusion for hyperspectral and LiDAR data classification, ” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 2, pp. 1437-1452, 2020.
    [12]
    P. Ghamisi, B. Höfle, and X. X. Zhu, “Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network, ” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 6, pp. 3011-3024, 2016.
    [13]
    Y. Zhu, W. Li, M. Zhang, Y. Pang, and Q. Du, “Joint feature extraction for multi-source data using similar double-concentrated network, ” Neurocomputing, vol. 450, pp. 70-79, 2021.
    [14]
    R. Hang, Z. Li, P. Ghamisi, D. Hong, G. Xia, and Q. Liu, “Classification of hyperspectral and LiDAR data using coupled CNNs, ” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 7, pp. 4939-4950, 2020.
    [15]
    X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks, ” in 2019 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), pp. 510-519, 2019.
    [16]
    S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift, ” in Proceedings of the 32nd International Conference on Machine Learning, Lille, France, pp. 448-456, 2015.
    [17]
    K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification, ” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp. 1026-1034, 2015.
    [18]
    S. Gao, M. M. Cheng, K. Zhao, X. Y. Zhang, and P. H. S. Torr, “Res2net: A new multi-scale backbone architecture, ” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 2, pp. 652-662, 2019.
    [19]
    P. Gader, A. Zare, R. Close, J. Aitken, and G. Tull, “MUUFL Gulfport hyperspectral and LiDAR airborne data set, ” University of Florida, Gainesville ,FL, USA, Tech. Rep. REP -2013-570, 2013.
    [20]
    B. Rasti, P. Ghamisi, and R. Gloaguen, “Hyperspectral and LiDAR fusion using extinction profiles and total variation component analysis, ” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3997-4007, 2017.
    [21]
    D. Hong, L. Gao, R. Hang, B. Zhang, and J. Chanussot, “Deep encoder-decoder networks for classification of hyperspectral and LiDAR data, ” IEEE Geoscience and Remote Sensing Letters, vol. 99, pp. 1-5, 2020.
    [22]
    X. Cao, F. Zhou, L. Xu, D. Meng, Z. Xu, and J. Paisley, “Hyperspectral image classification with Markov random fields and a convolutional neural network, ” IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2354-2367, 2018.
    [23]
    X. Xu, W. Li, Q. Ran, D. Qian, and Z. Bing, “Multisource remote sensing data classification based on convolutional neural network, ” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 2, pp. 937-949, 2017.
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