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
An improved algorithm for multi-polarization reconstruction from compact polarimetry （CP） is proposed. According to two fundamental assumptions in compact polarimetric reconstruction, two improvements are proposed. ...An improved algorithm for multi-polarization reconstruction from compact polarimetry （CP） is proposed. According to two fundamental assumptions in compact polarimetric reconstruction, two improvements are proposed. Firstly, the four-component model-based decomposition algorithm is modified with a new volume scattering model. The decomposed helix scattering component is then used to deal with the non-reflection symmetry condition in compact polarimetric measurements. Using the decomposed power and considering the scattering mechanism of each component, an average relationship between copolarized and crosspolarized channels is developed over the original polarization state extrapolation model. E-SAR polarimetric data acquired over the Oberpfaffenhofen area and JPL/AIRSAR polarimetric data acquired over San Francisco are used for verification, and good reconstruction results are obtained, demonstrating the effectiveness of the proposed algorithm.展开更多
Hybrid-polarimetric SAR(synthetic aperture radar) is a new SAR mode, with relatively simple architecture, low cost, and wide swath, which will be carried by several Earth-observing systems from now to the near future....Hybrid-polarimetric SAR(synthetic aperture radar) is a new SAR mode, with relatively simple architecture, low cost, and wide swath, which will be carried by several Earth-observing systems from now to the near future. Here, we show how the second Stokes parameter of hybrid-polarimetric SAR can be employed to detect oil on the ocean surface using the classic well-known Otsu threshold methodology, in relation to contributions from different polarizations and dampening effects on backscatter intensity, neglecting the specific scattering mechanisms and oil types for an oil-covered surface. The detection methodology is demonstrated to be reliable in three example cases: oil-on-water experiments conducted by the Norwegian Clean Seas Association, natural oil seeps from the Gulf of Mexico, and observations from the Deep Water Horizon oil spill disaster in 2010.展开更多
基于模型的目标分解是极化合成孔径雷达(SAR)的一个重要应用,基于模型目标分解依赖于极化数据仅能获得各散射机制的能量信息。该文将基于模型的分解技术应用到π/4模式的简缩极化干涉SAR(C-Pol In SAR)数据中,对互相关矩阵进行分解,在...基于模型的目标分解是极化合成孔径雷达(SAR)的一个重要应用,基于模型目标分解依赖于极化数据仅能获得各散射机制的能量信息。该文将基于模型的分解技术应用到π/4模式的简缩极化干涉SAR(C-Pol In SAR)数据中,对互相关矩阵进行分解,在获得各散射机制功率的同时获得对应的散射相位中心。该文首先推导出3种散射机制π/4简缩极化SAR干涉观测下散射模型,然后运用数值计算方法进行目标分解,最终求解出各散射机制的功率贡献及相位中心高度信息。仿真数据验证了该算法的有效性,分析了不同波段及不同地表参数对分解结果的影响。展开更多
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。
基金supported by the National Natural Science Foundation of China(41171317)the State Key Program of the Natural Science Foundation of China(61132008)the Research Foundation of Tsinghua University
文摘An improved algorithm for multi-polarization reconstruction from compact polarimetry （CP） is proposed. According to two fundamental assumptions in compact polarimetric reconstruction, two improvements are proposed. Firstly, the four-component model-based decomposition algorithm is modified with a new volume scattering model. The decomposed helix scattering component is then used to deal with the non-reflection symmetry condition in compact polarimetric measurements. Using the decomposed power and considering the scattering mechanism of each component, an average relationship between copolarized and crosspolarized channels is developed over the original polarization state extrapolation model. E-SAR polarimetric data acquired over the Oberpfaffenhofen area and JPL/AIRSAR polarimetric data acquired over San Francisco are used for verification, and good reconstruction results are obtained, demonstrating the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.41306189)the Knowledge Innovative Program of the Chinese Academy of Sciences+2 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Canadian Program on Energy Research and Developmentthe Canadian Space Agency GRIP initiative
文摘Hybrid-polarimetric SAR(synthetic aperture radar) is a new SAR mode, with relatively simple architecture, low cost, and wide swath, which will be carried by several Earth-observing systems from now to the near future. Here, we show how the second Stokes parameter of hybrid-polarimetric SAR can be employed to detect oil on the ocean surface using the classic well-known Otsu threshold methodology, in relation to contributions from different polarizations and dampening effects on backscatter intensity, neglecting the specific scattering mechanisms and oil types for an oil-covered surface. The detection methodology is demonstrated to be reliable in three example cases: oil-on-water experiments conducted by the Norwegian Clean Seas Association, natural oil seeps from the Gulf of Mexico, and observations from the Deep Water Horizon oil spill disaster in 2010.
文摘基于模型的目标分解是极化合成孔径雷达(SAR)的一个重要应用,基于模型目标分解依赖于极化数据仅能获得各散射机制的能量信息。该文将基于模型的分解技术应用到π/4模式的简缩极化干涉SAR(C-Pol In SAR)数据中,对互相关矩阵进行分解,在获得各散射机制功率的同时获得对应的散射相位中心。该文首先推导出3种散射机制π/4简缩极化SAR干涉观测下散射模型,然后运用数值计算方法进行目标分解,最终求解出各散射机制的功率贡献及相位中心高度信息。仿真数据验证了该算法的有效性,分析了不同波段及不同地表参数对分解结果的影响。
