文献类型：期刊文献

英文题名：An Advanced Approach for Understory Terrain Extraction Utilizing TomoSAR and MCSF Algorithm

作者：Xi, Bin[1] Zhang, Yu[1] Li, Wenmei[1,2] Zhao, Lei[3,4] Xu, Kunpeng[3,4] Ma, Yunmei[3,4] He, Yuhong[5]

第一作者：Xi, Bin

通信作者：Li, WM[1];Li, WM[2];Zhao, L[3]

机构：[1]Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing 210003, Peoples R China;[2]Nanjing Univ Posts & Telecommun, Hlth Big Data Anal & Locat Serv Engn Lab Jiangsu P, Nanjing 210003, Peoples R China;[3]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[4]NFGA, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[5]Univ Toronto, Dept Geog Geomat & Environm, Mississauga, ON L5L 1C6, Canada

年份：2025

卷号：22

外文期刊名：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

收录：;EI(收录号：20251818358214);Scopus(收录号：2-s2.0-105004078464);WOS:【SCI-EXPANDED(收录号:WOS:001488054300013)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 42071414, Grant 32471867, and Grant 32371869; inpart by the Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People's Republic of China under Grant KLSMNR-K202201 and Grant 202305, in part by the Open Fund of State Key Laboratory of Remote Sensing Science under Grant OFSLRSS202202; and in part by the Key Research and Development Program of Tianjin under Grant 22YFYSHZ00250.

语种：英文

外文关键词：Forestry; Point cloud compression; Data mining; Synthetic aperture radar; Filtering; Backscatter; Laser radar; Accuracy; Scattering; Remote sensing; Forests; modified-cloth-simulation-filtering (MCSF); point cloud; tomographic SAR (TomoSAR); understory terrain

摘要：The understory terrain is an essential component of forest vertical structure and ecosystem health, providing crucial insights for resource assessment and forestry surveys. This letter proposes a novel method for extracting understory terrain through forest backscattering power profiles and the modified cloth simulation filtering (MCSF) algorithm. It innovatively reconstructs synthetic aperture radar (SAR) signals into a 3-D point cloud, eliminating sidelobe signals to reduce noise while only retaining the mainlobe signals. The MCSF algorithm is subsequently utilized to extract ground and nonground points based on the vertical distribution of the mainlobe signals. The extracted ground points offer a more precise representation of actual terrain conditions. The feasibility of the method was validated utilizing airborne P-band multi baseline SAR data obtained from the Saihanba test site in Hebei Province. The outcomes clearly indicate that our approach exhibits superior correlation (0.999) and a smaller root mean square error (RMSE) (3.07 m) in comparison to conventional methods when compared with the reference digital elevation model (DEM).