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
Rice is an important food crop for human beings.Accurately distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimati...Rice is an important food crop for human beings.Accurately distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern agriculture.Compact polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and mapping.Currently,the use of machine learning methods to build classification models is a controversial topic.In this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice paddies.First,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data sets.Second,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data sets.In addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice paddies.The results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than 0.937.Among them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,respectively.For the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.展开更多
森林区域竖直结构参数的反演是极化干涉雷达的一个重要应用,基于RVo G模型,运用全极化干涉数据可成功获得森林区域的地形估计及树高反演。该文将基于单基线简缩极化干涉SAR(C-Pol In SAR)数据对森林区域进行林下地形估计及树高反演。推...森林区域竖直结构参数的反演是极化干涉雷达的一个重要应用,基于RVo G模型,运用全极化干涉数据可成功获得森林区域的地形估计及树高反演。该文将基于单基线简缩极化干涉SAR(C-Pol In SAR)数据对森林区域进行林下地形估计及树高反演。推导单基线简缩极化干涉相干系数及相干区域,根据相干区域进行直线拟合,提出简缩极化干涉数据下的地形相位判别准则及体散射去相干估计方法,然后完成树高反演。通过L,P波段仿真数据以及实测机载数据对上述方法进行验证,获得正确地形及树高。简缩极化发射波极化状态不唯一,因此该文详细分析不同参数的椭圆极化对地形及树高等参数估计的影响,研究表明地形树高受椭圆极化波影响较小,也验证了估计方法的稳定性。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.
基金funded in part by the National Natural Science Foundation of China(Grant No.41871272).
文摘Rice is an important food crop for human beings.Accurately distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern agriculture.Compact polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and mapping.Currently,the use of machine learning methods to build classification models is a controversial topic.In this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice paddies.First,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data sets.Second,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data sets.In addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice paddies.The results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than 0.937.Among them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,respectively.For the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.
文摘森林区域竖直结构参数的反演是极化干涉雷达的一个重要应用,基于RVo G模型,运用全极化干涉数据可成功获得森林区域的地形估计及树高反演。该文将基于单基线简缩极化干涉SAR(C-Pol In SAR)数据对森林区域进行林下地形估计及树高反演。推导单基线简缩极化干涉相干系数及相干区域,根据相干区域进行直线拟合,提出简缩极化干涉数据下的地形相位判别准则及体散射去相干估计方法,然后完成树高反演。通过L,P波段仿真数据以及实测机载数据对上述方法进行验证,获得正确地形及树高。简缩极化发射波极化状态不唯一,因此该文详细分析不同参数的椭圆极化对地形及树高等参数估计的影响,研究表明地形树高受椭圆极化波影响较小,也验证了估计方法的稳定性。
