文献类型：期刊文献

英文题名：Forest Height Extraction Based on TomoSAR Technique Using a Novel Phase Error Correction Method

作者：Xu, Kunpeng[1] Zhao, Lei[2] Chen, Erxue[2] Wang, Changcheng[3] Wan, Jie[3] Fan, Yaxiong[2] Wang, Jian[2] Ma, Yunmei[2] Song, Qing[2] Huang, Pingping[1] Li, Zengyuan[2]

第一作者：Xu, Kunpeng

通信作者：Zhao, L[1]

机构：[1]Inner Mongolia Univ Technol, Coll Informat Engn, Hohhot 010051, Peoples R China;[2]Chinese Acad Forestry, Key Lab Forestry Remote Sensing & Informat Syst, State Key Lab Efficient Prod Forest Resources, Inst Forest Resource Informat Tech,NFGA, Beijing, Peoples R China;[3]Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China

年份：2025

卷号：63

外文期刊名：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

收录：;EI(收录号：20253018825495);Scopus(收录号：2-s2.0-105011082050);WOS:【SCI-EXPANDED(收录号:WOS:001534516500003)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB3902600, in part by the National Natural Science Foundation of China under Grant 32471867 and Grant U22A2010, and in part by the Civil Space Program of China under Grant D010206.

语种：英文

外文关键词：Forestry; Tomography; Error correction; Optimization; Orbits; Synthetic aperture radar; Market research; Entropy; Robustness; Data mining; Autofocusing algorithm; forest height; phase error correction; synthetic aperture radar (SAR); tomography

摘要：Tomography synthetic aperture radar (TomoSAR) is a cutting-edge radar observation technique that has the ability to produce 3-D images and can effectively extract forest vertical structure parameters, including forest height, a key forest parameter closely related to forest biomass and carbon storage. However, the phase errors in the TomoSAR data are unavoidable due to elements such as orbit errors, which can seriously affect the quality of tomographic imaging and the accuracy of forest parameter extraction. To address this issue, various methods have been proposed. Nevertheless, they still exhibit restrictions when addressing phase errors with complex trends. To solve such a problem, a novel method was developed and implemented in this article, which includes two steps and removes parts of the phase errors with different trends sequentially. First, a wavelet decomposition and polynomial fitting-based approach were applied to each track to remove the slowly but significantly spatially varying part of the phase errors. Second, the modified autofocusing (MA) algorithm is proposed to correct the remaining phase errors, which adopted the 2-D image entropy as the optimization indicator, providing stronger robustness compared with the traditional indicator. Furthermore, in order to overcome the initial value dependency of the traditional search method, the proposed autofocusing algorithm used the particle swarm algorithm as a search engine. After the phase error correction, the forest height was extracted by identifying upper and lower boundaries of the forest from the corrected TomoSAR profiles. Two P-band datasets obtained in North China are adopted to examine the proposed phase error correction method. Experimental results show that, compared with the traditional autofocusing algorithm, the proposed method can achieve higher quality tomographic imaging results. On the basis of TomoSAR imaging, higher precision forest height extraction is obtained based on the new method.