Affected by the insufficient information of single baseline observation data,the three-stage method assumes the Ground-to-Volume Ratio(GVR)to be zero so as to invert the vegetation height.However,this assumption intro...Affected by the insufficient information of single baseline observation data,the three-stage method assumes the Ground-to-Volume Ratio(GVR)to be zero so as to invert the vegetation height.However,this assumption introduces much biases into the parameter estimates which greatly limits the accuracy of the vegetation height inversion.Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR.Nevertheless,the similar model parameter values in a multi-baseline model often lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm.To this end,we propose a new step-by-step inversion method applied to the multi-baseline observations.Firstly,an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data,and the regularized estimates of model parameters are obtained by regularization method.Then,the reliable estimates of GVR are determined by the MSE(mean square error)analysis of each regularized parameter estimation.Secondly,the estimated GVR is used to extracts the pure volume coherence,and then the vegetation height parameter is inverted from the pure volume coherence by least squares estimation.The experimental results show that the new method can improve the vegetation height inversion result effectively.The inversion accuracy is improved by 26%with respect to the three-stage method and the conventional solution of multi-baseline.All of these have demonstrated the feasibility and effectiveness of the new method.展开更多
[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using L...[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using Lensphoto multi-baseline digital close-range photogrammetry system,and through comparing with measured data of Total Station,the error and accuracy of photogrammetry data were analyzed.[Result] The absolute error of X,Y and Z coordinate was 0-0.014,0-0.018 and 0-0.004 m respectively,and the relative error of X,Y and Z coordinate was less than 0.145%.The significance test of pairs for the photogrammetry data and measured data of Total Station indicated that the space coordinate data of stumpage were accurately measured by using the multi-baseline digital close-range photogrammetry method,and the photogrammetry data meet the need of space coordinate measurement for virtual plant growth simulation.[Conclusion] This study had provided theoretical basis for the growth measurement of virtual plant growth simulation.展开更多
The linear multi-baseline stereo system introduced by the CMU-RI group has been proven to be a very effective and robust stereovision system. However, most traditional stereo rectification algorithms are all designed ...The linear multi-baseline stereo system introduced by the CMU-RI group has been proven to be a very effective and robust stereovision system. However, most traditional stereo rectification algorithms are all designed for binocular stereovision system, and so, cannot be applied to a linear multi-baseline system. This paper presents a simple and intuitional method that can simultaneously rectify all the cameras in a linear multi-baseline system. Instead of using the general 8-parameter homography transform, a two-step virtual rotation method is applied for rectification, which results in a more specific transform that has only 3 parameters, and more stability. Experimental results for real stereo images showed the presented method is efficient.展开更多
This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optima...This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optimal path-following strategy based on phase quality estimate function. The enhanced joint phase gradient estimator can accurately and effectively extract the phase gradient information of wrapped pixels from noisy interferograms, which greatly increases the performances of the proposed method. The optimal path-following strategy ensures that the proposed algorithm simultaneously performs noise suppression and phase unwrapping along the pixels with high-reliance to the pixels with low-reliance. Accordingly, the proposed algorithm can be predicted to obtain better results, with respect to some other algorithms, as will be demonstrated by the results obtained from synthetic data.展开更多
This paper proposes a new multi-baseline extended particle filtering phase unwrapping algorithm which combines an extended particle filter with an amended matrix pencil model and a quantized path-following strategy. T...This paper proposes a new multi-baseline extended particle filtering phase unwrapping algorithm which combines an extended particle filter with an amended matrix pencil model and a quantized path-following strategy. The contributions to multibaseline synthetic aperture radar(SAR) interferometry are as follows: a new recursive multi-baseline phase unwrapping model based on an extended particle filter is built, and the amended matrix pencil model is used to acquire phase gradient information with a higher precision and lower computational cost, and the quantized path-following strategy is introduced to guide the proposed phase unwrapping procedure to efficiently unwrap wrapped phase image along the paths routed by a phase derivative variance map.展开更多
基金National Natural Science Foundation of China(No.42104025)China Postdoctoral Science Foundation(No.2021M702509)+3 种基金Natural Resources Sciences and Technology Project of Hunan Province(No.2022-07)Surveying and Mapping Basic Research Foundation of Key Laboratory of Geospace Environment and Geodesy,Ministry of Education(No.20-01-04)Natural Science Foundation of Hunan Province(No.2024JJ5144)Open Fund of Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway(Changsha University of Science&Technology,No.kfj190805).
文摘Affected by the insufficient information of single baseline observation data,the three-stage method assumes the Ground-to-Volume Ratio(GVR)to be zero so as to invert the vegetation height.However,this assumption introduces much biases into the parameter estimates which greatly limits the accuracy of the vegetation height inversion.Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR.Nevertheless,the similar model parameter values in a multi-baseline model often lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm.To this end,we propose a new step-by-step inversion method applied to the multi-baseline observations.Firstly,an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data,and the regularized estimates of model parameters are obtained by regularization method.Then,the reliable estimates of GVR are determined by the MSE(mean square error)analysis of each regularized parameter estimation.Secondly,the estimated GVR is used to extracts the pure volume coherence,and then the vegetation height parameter is inverted from the pure volume coherence by least squares estimation.The experimental results show that the new method can improve the vegetation height inversion result effectively.The inversion accuracy is improved by 26%with respect to the three-stage method and the conventional solution of multi-baseline.All of these have demonstrated the feasibility and effectiveness of the new method.
基金Supported by National Natural Science Foundation of China(30770401)National Eleventh Five-Year Plan for Forestry Scienceand Technology Support Topics(2006BADO3A0505)~~
文摘[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using Lensphoto multi-baseline digital close-range photogrammetry system,and through comparing with measured data of Total Station,the error and accuracy of photogrammetry data were analyzed.[Result] The absolute error of X,Y and Z coordinate was 0-0.014,0-0.018 and 0-0.004 m respectively,and the relative error of X,Y and Z coordinate was less than 0.145%.The significance test of pairs for the photogrammetry data and measured data of Total Station indicated that the space coordinate data of stumpage were accurately measured by using the multi-baseline digital close-range photogrammetry method,and the photogrammetry data meet the need of space coordinate measurement for virtual plant growth simulation.[Conclusion] This study had provided theoretical basis for the growth measurement of virtual plant growth simulation.
文摘The linear multi-baseline stereo system introduced by the CMU-RI group has been proven to be a very effective and robust stereovision system. However, most traditional stereo rectification algorithms are all designed for binocular stereovision system, and so, cannot be applied to a linear multi-baseline system. This paper presents a simple and intuitional method that can simultaneously rectify all the cameras in a linear multi-baseline system. Instead of using the general 8-parameter homography transform, a two-step virtual rotation method is applied for rectification, which results in a more specific transform that has only 3 parameters, and more stability. Experimental results for real stereo images showed the presented method is efficient.
基金supported by the National Natural Science Foundation of China(4120147961261033+2 种基金61461011)the Guangxi Natural Science Foundation(2014GXNSFBA118273)the Dean Project of Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing(GXKL061503)
文摘This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optimal path-following strategy based on phase quality estimate function. The enhanced joint phase gradient estimator can accurately and effectively extract the phase gradient information of wrapped pixels from noisy interferograms, which greatly increases the performances of the proposed method. The optimal path-following strategy ensures that the proposed algorithm simultaneously performs noise suppression and phase unwrapping along the pixels with high-reliance to the pixels with low-reliance. Accordingly, the proposed algorithm can be predicted to obtain better results, with respect to some other algorithms, as will be demonstrated by the results obtained from synthetic data.
基金supported by the National Natural Science Foundation of China(4166109261461011)the Natural Science Foundation of Guangxi Province(2014GXNSFBA118273)
文摘This paper proposes a new multi-baseline extended particle filtering phase unwrapping algorithm which combines an extended particle filter with an amended matrix pencil model and a quantized path-following strategy. The contributions to multibaseline synthetic aperture radar(SAR) interferometry are as follows: a new recursive multi-baseline phase unwrapping model based on an extended particle filter is built, and the amended matrix pencil model is used to acquire phase gradient information with a higher precision and lower computational cost, and the quantized path-following strategy is introduced to guide the proposed phase unwrapping procedure to efficiently unwrap wrapped phase image along the paths routed by a phase derivative variance map.
