以第一或通讯作者发表学术论文100余篇,其中SCI论文50余篇。10篇代表性论文如下:
(1)Chen CF*, Yang Z., Pan H., Li Y., Hao J. TFRSUB: A terrain-feature retention and spatial uniformity balancing method for simplifying LiDAR ground point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2026, 232, 389-407.
(2)Chen CF*, Wu H, Yang Z, Li Y. Adaptive coarse-to-fine clustering and terrain feature-aware-based method for reducing LiDAR terrain point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 200: 89-105.
(3)Chen CF*, Yan C Q, Cao X W, Guo J Y, Dai H L. A greedy-based multiquadric method for LiDAR-derived ground data reduction. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 102: 110-121.
(4)Chen CF*, Li Y Y, Li W, Dai H L. A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 82: 1-9.
(5)Chen CF*, Wang X, Hao J and Li Y. HFSM: High-Fidelity Surface Modeling for Accurate DEMs With Terrain Discontinuity Preservation. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 1-9.
(6)Chen CF*, Li Y Y, Zhao N, Yan C Q. Robust interpolation of DEMs from Lidar-derived elevation data. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 1059 - 1068.
(7)Chen CF*, Liu Y, Li Y, Chen D*. Explainable artificial intelligence framework for urban global digital elevation model correction based on the SHapley additive explanation-random forest algorithm considering spatial heterogeneity and factor optimization. International Journal of Applied Earth Observation and Geoinformation, 2024, 129: 103843.
(8)Chen CF*, Gao Y, Li Y, Bei Y. Structure tensor-based interpolation for the derivation of accurate digital elevation models. CATENA, 2022, 208: 105733.
(9)Chen CF*, Hao J, Yang S and Li Y. Blending daily satellite precipitation product and rain gauges using stacking ensemble machine learning with the consideration of spatial heterogeneity. Journal of Hydrology, 2025, 658: 133223.
(10)Chen CF*, He Q, Li Y. Downscaling and merging multiple satellite precipitation products and gauge observations using random forest with the incorporation of spatial autocorrelation. Journal of Hydrology, 2024, 632: 130919.