Laser Journal, Volume. 45, Issue 6, 233(2024)
High-resolution 3D lidar imaging target detection method based on deep learning
[1] [1] Sun Weiwei, Du Qian. Graph-regularized fast and robust principal component analysis for hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6): 3185-3195.
[2] [2] Gao Peichao, Wang Jicheng, Zhang Hong, et al. Boltzmann entropy-based unsupervised band selection for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(3): 462-466.
[3] [3] Casa R, Upreti D, Palombo A, et al. Evaluation and exploitation of retrieval algorithms for estimating biophysical crop variables using Sentinel-2, Venus, and PRISMA satellite data[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(4): 79-88.
[4] [4] Chang C I, Wang Su. Constrained band selection for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(6): 1575-1585.
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MIAO Changfen. High-resolution 3D lidar imaging target detection method based on deep learning[J]. Laser Journal, 2024, 45(6): 233
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Received: Sep. 14, 2023
Accepted: Nov. 26, 2024
Published Online: Nov. 26, 2024
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