SAR Tomography with Improved Non-Local Means Filtering Based on Adaptive Window

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 interferograms.Apart 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 aspect.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 interferogram.Simultaneously,a directional window is incorporated to align SAR interferograms,achieving the dual objective of preserving filtering standards and retaining detailed information.Experimental 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.

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

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 conditions.Recent 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 sites.For purpose of solving this problem,we investigate the possibility of using Li DAR derived AGB to help training the two parameters.Experimental results obtained by processing data from the Tropi SAR campaign support the feasibility of the proposed concept.This 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 included.The resulting backscattered power variation at 30 m is within 1.5 d B.For 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.