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
At present,the principal data processing methods involving complex observations are based on two strategies according to characteristics of the observation process,i.e.,step-by-step and direct resolution.However,these...At present,the principal data processing methods involving complex observations are based on two strategies according to characteristics of the observation process,i.e.,step-by-step and direct resolution.However,these strategies have some limitations,e.g.they cannot consider statistical observation error information,redundant observations and so on.This paper applies least squares methods to complex data processing to extend surveying adjustment theory from real to complex number space.We compared the two adjustment criteria for a complex domain in a quantitative way.In order to understand the effectiveness of complex least squares,tree height inversion from PolInSAR data is taken as an example.We firstly established both a complex adjustment function model and a stochastic model for PolInSAR tree height inversion,and then applied the complex least squares method to estimate tree height.Results show that the complex least squares approach is reliable and outperforms other classic tree height retrieval methods;the method is simple and easy to implement.展开更多
Aiming to solve the misclassification problems of unsupervised polarimetric Wishart clas- sification algorithm based on Freeman decomposition, an unsupervised Polarimetric Synthetic Aper- ture Radar （SAR） Interferot...Aiming to solve the misclassification problems of unsupervised polarimetric Wishart clas- sification algorithm based on Freeman decomposition, an unsupervised Polarimetric Synthetic Aper- ture Radar （SAR） Interferotnery （PolInSAR） classification algorithm based on optimal coherence set parameters is studied and proposed. This algorithm uses the result of Freeman decomposition to divide the image into three basic categories including surface scattering, volume scattering, and double-bounce Then, the PolInSAR optimal coherence set parameters are used to finely divide each of the three basic categories into 9 categories, and the whole image is divided into 27 categories. Because both the Freeman decomposition result and optimal coherence set parameters indicate specific scattering characteristics, the whole image is merged into 16 categories based on physical meaning. At last, the Wishart cluster is employed to obtain the final classification result. To preserve the purity of scattering characteristics, pixels with similar scattering characteristics are restricted to be classified with other pixels. The final classification results effectively resolve the misclassification problem, not only the buildings can be effectively distinguished from vegetation in urban areas, but also the road is well distinguished from grass. In this paper, the E-SAR PolInSAR data of German Aerospace Center （DLR） are used to verify the effectiveness of the algorithm.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The National Natural Science Foundation of China(41274010,40974007,40901172)National Key Basic Research and Development Program of China(2012AA121301)+2 种基金Hunan Provincial Natural Science Foundation of China(12JJ4035)Postgraduate Autonomous Exploration Project of Central South University(2013zzts055)China Scholarship Council(201406370079).
文摘At present,the principal data processing methods involving complex observations are based on two strategies according to characteristics of the observation process,i.e.,step-by-step and direct resolution.However,these strategies have some limitations,e.g.they cannot consider statistical observation error information,redundant observations and so on.This paper applies least squares methods to complex data processing to extend surveying adjustment theory from real to complex number space.We compared the two adjustment criteria for a complex domain in a quantitative way.In order to understand the effectiveness of complex least squares,tree height inversion from PolInSAR data is taken as an example.We firstly established both a complex adjustment function model and a stochastic model for PolInSAR tree height inversion,and then applied the complex least squares method to estimate tree height.Results show that the complex least squares approach is reliable and outperforms other classic tree height retrieval methods;the method is simple and easy to implement.
文摘Aiming to solve the misclassification problems of unsupervised polarimetric Wishart clas- sification algorithm based on Freeman decomposition, an unsupervised Polarimetric Synthetic Aper- ture Radar （SAR） Interferotnery （PolInSAR） classification algorithm based on optimal coherence set parameters is studied and proposed. This algorithm uses the result of Freeman decomposition to divide the image into three basic categories including surface scattering, volume scattering, and double-bounce Then, the PolInSAR optimal coherence set parameters are used to finely divide each of the three basic categories into 9 categories, and the whole image is divided into 27 categories. Because both the Freeman decomposition result and optimal coherence set parameters indicate specific scattering characteristics, the whole image is merged into 16 categories based on physical meaning. At last, the Wishart cluster is employed to obtain the final classification result. To preserve the purity of scattering characteristics, pixels with similar scattering characteristics are restricted to be classified with other pixels. The final classification results effectively resolve the misclassification problem, not only the buildings can be effectively distinguished from vegetation in urban areas, but also the road is well distinguished from grass. In this paper, the E-SAR PolInSAR data of German Aerospace Center （DLR） are used to verify the effectiveness of the algorithm.
