The paramount importance and multi-purpose applications of underlying topography over forest areas have gained widespread recognition over recent decades, bringing about a variety of experimental studies on accurate u...The paramount importance and multi-purpose applications of underlying topography over forest areas have gained widespread recognition over recent decades, bringing about a variety of experimental studies on accurate underlying topography mapping. The highly spatial and temporal dynamics of forest scenarios makes traditional measuring techniques difficult to construct the precise underlying topography surface. Microwave remote sensing has been demonstrated as a promising technique to retrieve the underlying topography over large areas within a limited period, including synthetic aperture radar interferometry(InSAR), polarimetric InSAR(PolInSAR) and tomographic SAR(TomoSAR). In this paper, firstly, the main principle of digital elevation model(DEM) generation by InSAR and SAR data acquisition over forest area are introduced. Following that, several methods of underlying topography extraction based on InSAR, PolInSAR, and TomoSAR are introduced and analyzed, as well as their applications and performance are discussed afterwards. Finally, four aspects of challenge are highlighted, including SAR data acquisition, error compensation and correction, scattering model reconstruction and solution strategy of multi-source data, which needs to be further addressed for robust underlying topography estimation.展开更多
Due to the strong penetrability,long-wavelength synthetic aperture radar(SAR)can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media.SAR tomography(TomoSAR)technology can resy...Due to the strong penetrability,long-wavelength synthetic aperture radar(SAR)can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media.SAR tomography(TomoSAR)technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights,providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects.As an emerging technology,it is different from the traditional interferometric SAR(InSAR)technology and has advantages in reconstructing the three-dimensional structure of the illuminated media.Over the past two decades,many TomoSAR methods have been proposed to improve the vertical resolution,aiming to distinguish the locations of different scatters in the unit pixel.In order to cope with the forest mission of European Space Agency(ESA)that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests,it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology.In this paper,we adopt three typical algorithms,namely,Capon,Multiple Signal Classification(MUSIC),and Compressed Sensing(CS),to evaluate the performance in forest height and underlying topography inversion.The P-band airborne full-polarization(FP)SAR data of LopèNational Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment.Furthermore,we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile.The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions.Compared with LiDAR results,the root-mean-square error(RMSE)of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m,which performs the best among the three algorithms.展开更多
The underlying topography and forest height play an indispensable role in many fields,including geomorphology,civil engineering construction,forest investigation,and the modeling of natural disasters.As a new microwav...The underlying topography and forest height play an indispensable role in many fields,including geomorphology,civil engineering construction,forest investigation,and the modeling of natural disasters.As a new microwave remote sensing technology with three-dimensional imaging capability,synthetic aperture radar(SAR)tomography(TomoSAR)has already been proven to be an important tool for underlying topography and forest height estimation.Many spectrum estimation methods have now been proposed for TomoSAR.However,most of the commonly used methods are susceptible to noise and inevitably produce sidelobes,resulting in a reduced accuracy for the inversion of forest structural parameters.In this paper,to solve this problem,a nonparametric spectrum estimation method with low sidelobes-the G-Pisarenko method-is introduced.This method performs a logarithmic operation on the covariance matrix to obtain the main scattering characteristics of the objects of interest while suppressing the noise as much as possible.The effectiveness of the proposed method is demonstrated by the use of both simulated data and P-band airborne SAR data from a tropical forest region in Gabon,Africa.The results show that the proposed method can reduce the sidelobes and improve the estimation accuracy for the underlying topography and forest height.展开更多
基金Projects(41820104005,41531068,41842059,41904004)supported by the National Natural Science Foundation of China。
文摘The paramount importance and multi-purpose applications of underlying topography over forest areas have gained widespread recognition over recent decades, bringing about a variety of experimental studies on accurate underlying topography mapping. The highly spatial and temporal dynamics of forest scenarios makes traditional measuring techniques difficult to construct the precise underlying topography surface. Microwave remote sensing has been demonstrated as a promising technique to retrieve the underlying topography over large areas within a limited period, including synthetic aperture radar interferometry(InSAR), polarimetric InSAR(PolInSAR) and tomographic SAR(TomoSAR). In this paper, firstly, the main principle of digital elevation model(DEM) generation by InSAR and SAR data acquisition over forest area are introduced. Following that, several methods of underlying topography extraction based on InSAR, PolInSAR, and TomoSAR are introduced and analyzed, as well as their applications and performance are discussed afterwards. Finally, four aspects of challenge are highlighted, including SAR data acquisition, error compensation and correction, scattering model reconstruction and solution strategy of multi-source data, which needs to be further addressed for robust underlying topography estimation.
基金supported by ESA-MOST Dragon Programme 5[grant number 59332].
文摘Due to the strong penetrability,long-wavelength synthetic aperture radar(SAR)can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media.SAR tomography(TomoSAR)technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights,providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects.As an emerging technology,it is different from the traditional interferometric SAR(InSAR)technology and has advantages in reconstructing the three-dimensional structure of the illuminated media.Over the past two decades,many TomoSAR methods have been proposed to improve the vertical resolution,aiming to distinguish the locations of different scatters in the unit pixel.In order to cope with the forest mission of European Space Agency(ESA)that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests,it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology.In this paper,we adopt three typical algorithms,namely,Capon,Multiple Signal Classification(MUSIC),and Compressed Sensing(CS),to evaluate the performance in forest height and underlying topography inversion.The P-band airborne full-polarization(FP)SAR data of LopèNational Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment.Furthermore,we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile.The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions.Compared with LiDAR results,the root-mean-square error(RMSE)of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m,which performs the best among the three algorithms.
基金supported in part by the National Natural Science Foundation of China[grant number 42101400],[grant number 42171387]in part by the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA19070202].
文摘The underlying topography and forest height play an indispensable role in many fields,including geomorphology,civil engineering construction,forest investigation,and the modeling of natural disasters.As a new microwave remote sensing technology with three-dimensional imaging capability,synthetic aperture radar(SAR)tomography(TomoSAR)has already been proven to be an important tool for underlying topography and forest height estimation.Many spectrum estimation methods have now been proposed for TomoSAR.However,most of the commonly used methods are susceptible to noise and inevitably produce sidelobes,resulting in a reduced accuracy for the inversion of forest structural parameters.In this paper,to solve this problem,a nonparametric spectrum estimation method with low sidelobes-the G-Pisarenko method-is introduced.This method performs a logarithmic operation on the covariance matrix to obtain the main scattering characteristics of the objects of interest while suppressing the noise as much as possible.The effectiveness of the proposed method is demonstrated by the use of both simulated data and P-band airborne SAR data from a tropical forest region in Gabon,Africa.The results show that the proposed method can reduce the sidelobes and improve the estimation accuracy for the underlying topography and forest height.
