• Infrared and Laser Engineering
  • Vol. 50, Issue 12, 20210112 (2021)
Leiguang Wang1、2, Ruozheng Geng3, Qinling Dai4, Jun Wang3, Chen Zheng5、*, and Zhitao Fu6
Author Affiliations
  • 1Institutes of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650224, China
  • 2Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming 650224, China
  • 3Forestry College, Southwest Forestry University, Kunming 650224, China
  • 4College of Art and Design, Southwest Forestry University, Kunming 650224, China
  • 5College of Mathematics and Statistic, Henan University, Kaifeng 475004, China
  • 6Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
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    DOI: 10.3788/IRLA20210112 Cite this Article
    Leiguang Wang, Ruozheng Geng, Qinling Dai, Jun Wang, Chen Zheng, Zhitao Fu. Conditional random field classification method based on hyperspectral-LiDAR fusion[J]. Infrared and Laser Engineering, 2021, 50(12): 20210112 Copy Citation Text show less
    References

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    [2] D Muram, S Prasad, F Pacifict, et al. Challenges and opportunities of multimodality and data fusion in remote sensing. Proceedings of the IEEE, 103, 1585-1601(2015).

    [3] B Rasti, P Ghamisi, R Gloaguen. Hyperspectral and LiDAR fusion using extinction profiles and total variation component analysis. IEEE Transactions on Geoscience and Remote Sensing, 55, 3997-4007(2017).

    [4] Qiong Cao, Ailong Ma, Yanfei Zhong, et al. Hyperspectral-LiDAR multi-level fusion urban land cover classification. National Remote Sensing Bulletin, 23, 892-903(2019).

    [5] Guojun Shi. Infrared image target recognition method based on joint characterization of depth feature. Infrared and Laser Engineering, 50, 20200399(2021).

    [6] Banghuan Hou, Minli Yao, Weimin Jia, et al. Hyperspectral image classification based on spatial structure preserving. Infrared and Laser Engineering, 46, 1228001(2017).

    [7] W Liao, A Pižurica, R Bellens, et al. Generalized graph-based fusion of hyperspectral and lidar data using morphological features. IEEE Geoscience and Remote Sensing Letters, 12, 552-556(2015).

    [8] P Ghamisi, B Rasti, J A Benediktsson. Multisensor composite kernels based on extreme learning machines. IEEE Geoscience and Remote Sensing Letters, 16, 196-200(2019).

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    [10] C Debes, A Merentitis, R Heremans, et al. Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2405-2418(2014).

    [11] L Ni, L Gao, S Li, et al. Edge-constrained Markov random field classification by integrating hyperspectral image with LiDAR data over urban areas. Journal of Applied Remote Sensing, 8, 085089(2014).

    [12] L Wang, X Huang, C Zheng, et al. A Markov random field integrating spectral dissimilarity and class co-occurrence dependency for remote sensing image classification optimization. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 223-239(2017).

    [13] C C Chang, C J Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27(2011).

    [14] B Feng, C Zhang, W Zhang, et al. Analyzing the role of spatial features when cooperating hyperspectral and LiDAR data for the tree species classification in a subtropical plantation forest area. Journal of Applied Remote Sensing, 14, 022213(2020).

    [15] Y Cheng, C Li, P Ghamisp, et al. Deep fusion of remote sensing data for accurate classification. IEEE Geoscience and Remote Sensing Letters, 14, 1253-1257(2017).

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    [1] Liying Wang, Ze You, Ji Wu, Mahamadou CAMARA. Airborne MS-LiDAR data classification by combining NDRI features and spatial correlation[J]. Infrared and Laser Engineering, 2023, 52(2): 20220376

    Leiguang Wang, Ruozheng Geng, Qinling Dai, Jun Wang, Chen Zheng, Zhitao Fu. Conditional random field classification method based on hyperspectral-LiDAR fusion[J]. Infrared and Laser Engineering, 2021, 50(12): 20210112
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