[1] Zhou C, Ling Q. GAF-Net: geometric contextual feature aggregation and adaptive fusion for large-scale point cloud semantic segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 5705815(2023).

[2] Chen L, Wu P H, Chitta K et al. End-to-end autonomous driving: challenges and frontiers[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 10164-10183(2024).

[3] Yu C C, Zhang Z H, Li H B et al. Meta-learning-based adversarial training for deep 3D face recognition on point clouds[J]. Pattern Recognition, 134, 109065(2023).

[4] Xu W T, Li X Z, Zhang Y S et al. Fine identification and characterization of rock mass discontinuities and its application using a digital photogrammetry system[J]. Acta Geodaetica et Cartographica Sinica, 51, 2093-2106(2022).

[5] Jones L, Hobbs P. The application of terrestrial LiDAR for geohazard mapping, monitoring and modelling in the British geological survey[J]. Remote Sensing, 13, 395(2021).

[6] Ha S M, Son M, Seong Y B. Active fault trace identification using a LiDAR high-resolution DEM: a case study of the central Yangsan Fault, Korea[J]. Remote Sensing, 14, 4838(2022).

[7] Ramalho de Oliveira L F, Lassiter H A, Wilkinson B et al. Moving to automated tree inventory: comparison of UAS-derived lidar and photogrammetric data with manual ground estimates[J]. Remote Sensing, 13, 72(2020).

[8] Guo J T, Liu Y H, Wu L X et al. A geometry- and texture-based automatic discontinuity trace extraction method for rock mass point cloud[J]. International Journal of Rock Mechanics and Mining Sciences, 124, 104132(2019).

[9] Eitel J U H, Höfle B, Vierling L A et al. Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences[J]. Remote Sensing of Environment, 186, 372-392(2016).

[10] Lague D, Brodu N, Leroux J. Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z)[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 10-26(2013).

[11] Franceschi M, Teza G, Preto N et al. Discrimination between marls and limestones using intensity data from terrestrial laser scanner[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 522-528(2009).

[12] Burton D, Dunlap D B, Wood L J et al. Lidar intensity as a remote sensor of rock properties[J]. Journal of Sedimentary Research, 81, 339-347(2011).

[13] Hartzell P, Glennie C, Biber K et al. Application of multispectral LiDAR to automated virtual outcrop geology[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 147-155(2014).

[14] Ding K X, Lu T, Fu W et al. Global-local transformer network for HSI and LiDAR data joint classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5541213(2022).

[15] Zhao G R, Ye Q L, Sun L et al. Joint classification of hyperspectral and LiDAR data using a hierarchical CNN and transformer[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 5500716(2022).

[16] Wang Y, Sun Y B, Liu Z W et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 38, 146(2019).

[17] Humair F, Abellan A, Carrea D et al. Geological layers detection and characterisation using high resolution 3D point clouds: example of a box-fold in the Swiss Jura Mountains[J]. European Journal of Remote Sensing, 48, 541-568(2015).

[18] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C], 77-85(2017).

[19] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[EB/OL]. https://arxiv.org/abs/1706.02413v1

[20] Jiang M Y, Wu Y R, Zhao T Q et al. PointSIFT: a SIFT-like network module for 3D point cloud semantic segmentation[EB/OL]. https://arxiv.org/abs/1807.00652v2

[21] Lu B, Liu Y W, Zhang Y H et al. Point cloud segmentation algorithm based on density awareness and self-attention mechanism[J]. Laser & Optoelectronics Progress, 61, 0811004(2024).

[22] Liu Y C, Fan B, Xiang S M et al. Relation-shape convolutional neural network for point cloud analysis[C], 8887-8896(2019).

[23] Liu Y Q, Ao J F, Pan Z T. DGPoint: a dynamic graph convolution network for point cloud semantic segmentation[J]. Laser & Optoelectronics Progress, 59, 1610014(2022).

[24] Xu M T, Ding R Y, Zhao H S et al. PAConv: position adaptive convolution with dynamic kernel assembling on point clouds[C], 3172-3181(2021).

[25] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[26] Cai Z Y, Li C L, Yu Y et al. Momentum accelerated unfolding network with spectral-spatial prior for computational spectral imaging[J]. Applied Soft Computing, 154, 111420(2024).

[27] Cai Z Y, Liu Z, Yu J et al. Reversible-prior-based spectral-spatial transformer for efficient hyperspectral image reconstruction[J]. International Journal on Semantic Web and Information Systems, 20, 1-22(2024).

[28] Zhang W, Zeng Z L, Fang Q et al. Enhanced point cloud segmentation method using position encoding and channel attention[J]. Laser & Optoelectronics, 62, 0415008(2025).

[29] Sanchiz-Viel N, Bretagne E, Mouaddib E M et al. Radiometric correction of laser scanning intensity data applied for terrestrial laser scanning[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 172, 1-16(2021).

[30] Bai J, Niu Z, Gao S et al. An exploration, analysis, and correction of the distance effect on terrestrial hyperspectral LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 60-83(2023).

[31] Marukatat S. Tutorial on PCA and approximate PCA and approximate kernel PCA[J]. Artificial Intelligence Review, 56, 5445-5477(2023).

[32] Nguyen H M, Demir B, Dalponte M. A weighted SVM-based approach to tree species classification at individual tree crown level using LiDAR data[J]. Remote Sensing, 11, 2948(2019).

[33] Li R, Zheng S Y, Duan C X et al. Classification of hyperspectral image based on double-branch dual-attention mechanism network[J]. Remote Sensing, 12, 582(2020).