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sar tomography with Improved Non-Local Means Filtering Based on Adaptive Window

Journal of Beijing Institute of Technology 2023年第6期32卷 670-684页

作者： Shenglei Wang Zhiyang Chen Yuanhao Li Cheng Hu Radar Research Laboratory School of Information and ElectronicsBeijing Institute of TechnologyBeijing 100081China Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of TechnologyBeijing 100081China

In order to mitigate speckle noise in synthetic aperture radar(sar)images and enhance the accuracy of sar tomography,non-local means(NL-means)filtering has been proven to be an effective method for improving the quality of sar *** from considerations like noise type and the definition of similarity,the size and shape of filtering windows are critical factors influencing the efficacy of NL-means filtering,yet there has been limited research on this *** paper introduces an enhanced NL-means filtering method based on adaptive windows,allowing for the automatic adjustment of filtering window size according to the amplitude information of the sar ***,a directional window is incorporated to align sar interferograms,achieving the dual objective of preserving filtering standards and retaining detailed *** results on interferogram filtering and tomography,based on Terrasar-X data,demonstrate that the proposed method effectively reduces phase noise while maintaining texture accuracy,thereby improving tomography quality.

关键词： NL-means filter adaptive window sar interferogram filtering sar tomography

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Comparision of Methods for Polarimetric sar tomography with Small Number of Baselines in Forested Areas

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Chinese Journal of Electronics 2019年第5期28卷 1073-1079页

作者： LIANG Lei ZHOU Chenghu LI Xinwu GAO Xizhang WU Wenjin State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Key Laboratory of Digital Earth Sciences Institute of Remote Sensing and Digital Earth (RADI) Chinese Academy of Sciences

Synthetic aperture radar tomography(Tomosar) provides scene reflectivity estimation of vegetation along elevation coordinates. However, the more multi-baselines acquisition, the longer the time span of acquisition, which will result in serious temporal decorrelation in forested area. In this way, we expect to use as smaller number of baselines as possible to obtain high estimation accuracy in elevation direction. We thus investigate the performance of Polarimetric sar tomography(Pol-Tomosar) with small number of baselines in forested areas. The results show that compressive sensingbased Pol-Tomosar has higher estimation accuracy in elevation direction than conventional Pol-Tomosar with small number of baselines.

关键词： Synthetic aperture radar(sar) Polarimetric sar sar tomography Compressive sensing

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Progresses on sar Remote Sensing of Tropical Forests:Forest Biomass Retrieval and Analysis of Changing Weather Conditions

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Journal of Geodesy and Geoinformation Science 2021年第1期4卷 88-93页

作者： Stefano TEBALDINI Xinwei YANG Yu BAI Mauro Mariotti D’ALESSANDRO Mingsheng LIAO Wen YANG DEIB Politecnico di MilanoMilan 20133Italy LIESMARS Wuhan UniversityWuhan 430079China School of Electronic Information Wuhan UniversityWuhan 430072China

This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation(ID:32278),sub-project Multi-baseline sar Processing for 3 D/4 D Reconstruction(ID:322782).The work here reported focuses on two important aspects of sar remote sensing of tropical forests,namely the retrieval of forest biomass and the assessment of effects due to changing weather *** studies have shown that by using sar tomography the backscattered power at 30 m layer above the ground is linearly correlated to the forest Above Ground Biomass(AGB).However,the two parameters that determine this linear relationship might vary for different tropical forest *** purpose of solving this problem,we investigate the possibility of using Li DAR derived AGB to help training the two *** results obtained by processing data from the Tropi sar campaign support the feasibility of the proposed *** analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging,for which we simulate BIOMASS repeat pass tomography using ground-based Tropi SCAT data with a revisit time of 3 days and rainy days *** resulting backscattered power variation at 30 m is within 1.5 d *** this forest site,this error is translated into an AGB error of about 50~80 t/hm^(2),which is 20%or less of forest AGB.

关键词： tropical forest biomass sar tomography Li DAR temporal decorrelation

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