In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Ape...In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Aperture Radar(SAR)decomposition.It uses a Polarimetric Interferometric Similarity Parameter(PISP)calculated from Polarimetric SAR Interferometry(PolInSAR)datasets to the scattering decomposition.The PISP is proposed to reveal the geometric sensitivity of SAR interferometry.It is defined by three optimized mechanisms obtained from PolInSAR datasets,therefore,it not only relates to the coherent scattering mechanism closely,but also sufficiently uses the phase and amplitude information.The PISP of building is high,and forest’s PISP is low.The proposed method uses the PISP as a judge condition to select different vegetation model adaptively.The decomposition results show the proposed method can effectively solve the vegetation ingredients overestimation problem.In addition,it is sensitive to the directional scattering.展开更多
森林树高的反演是极化干涉合成孔径雷达(polarimetric SAR interferometry,PolInSAR)领域研究的热点,但时间去相关的存在引起了散射体的变化,降低了树高反演的性能,其中土壤含水量的改变是引起时间去相关的因素之一。本文利用PolSARpro...森林树高的反演是极化干涉合成孔径雷达(polarimetric SAR interferometry,PolInSAR)领域研究的热点,但时间去相关的存在引起了散射体的变化,降低了树高反演的性能,其中土壤含水量的改变是引起时间去相关的因素之一。本文利用PolSARpro生成不同土壤含水量的极化SAR模拟数据,通过三阶段算法进行树高反演实验。结果表明:土壤含水量的变化会导致反演结果产生一定偏差,但此偏差相对较小,已远远小于算法本身造成的误差。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
文摘In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Aperture Radar(SAR)decomposition.It uses a Polarimetric Interferometric Similarity Parameter(PISP)calculated from Polarimetric SAR Interferometry(PolInSAR)datasets to the scattering decomposition.The PISP is proposed to reveal the geometric sensitivity of SAR interferometry.It is defined by three optimized mechanisms obtained from PolInSAR datasets,therefore,it not only relates to the coherent scattering mechanism closely,but also sufficiently uses the phase and amplitude information.The PISP of building is high,and forest’s PISP is low.The proposed method uses the PISP as a judge condition to select different vegetation model adaptively.The decomposition results show the proposed method can effectively solve the vegetation ingredients overestimation problem.In addition,it is sensitive to the directional scattering.
文摘森林树高的反演是极化干涉合成孔径雷达(polarimetric SAR interferometry,PolInSAR)领域研究的热点,但时间去相关的存在引起了散射体的变化,降低了树高反演的性能,其中土壤含水量的改变是引起时间去相关的因素之一。本文利用PolSARpro生成不同土壤含水量的极化SAR模拟数据,通过三阶段算法进行树高反演实验。结果表明:土壤含水量的变化会导致反演结果产生一定偏差,但此偏差相对较小,已远远小于算法本身造成的误差。
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.
