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
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
The conjugate hydrocyanation of chalcone derivatives using ethyl cyanoacetate as an organic cyanide source at room temperature under open air and transition metal-free conditions was described. The protocol has advant...The conjugate hydrocyanation of chalcone derivatives using ethyl cyanoacetate as an organic cyanide source at room temperature under open air and transition metal-free conditions was described. The protocol has advantages of using relatively cheap, less toxic, stable and easy-to-handle cyanating reagent, high yield, and mild reaction condi- tion.展开更多
Calculated parameters(soil layer resistivity,soil layer thickness,and the number of soil layers)of horizontally layered soil are usually obtained based on the measured apparent resistivity under different measurement ...Calculated parameters(soil layer resistivity,soil layer thickness,and the number of soil layers)of horizontally layered soil are usually obtained based on the measured apparent resistivity under different measurement distances,which are significant for the design,operation,and maintenance of grounding grids.Most existing calculation methods of soil parameters are just trying to make the calculation results approach the measurement data,ignoring the relationship among measurement data,the calculated soil parameters,and grounding parameters,which would increase the workload of the measurement.To better balance the distance range of the measurement data and the influence of the calculated horizontally layered soil on grounding parameters,this paper systematically studies the relationship among measured apparent soil resistivity,calculated horizontally layered soil parameters,and grounding parameters.The basic theories of apparent resistivity measurement,soil parameter calculation,and grounding parameter calculation are given,the influence of soil layer thickness on the measured apparent resistivity is studied,and the influence of the calculated soil parameters on the grounding resistance of different grounding models is analysed.Based on different scales of grounding grids,the results give a corresponding reference distance range of measured apparent soil resistivity.Therefore,this paper can help decrease the workload of soil resistivity measurement during grounding parameters analysis,which has far-reaching engineering significance.展开更多
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%.
基金The authors thank the National Natural Science Foundation of China （Nos. 21462038, 21362034） and Key Laboratory of Eco-Environment-Related Polymer Materials of Ministry of Education for the financial support of this work.
文摘The conjugate hydrocyanation of chalcone derivatives using ethyl cyanoacetate as an organic cyanide source at room temperature under open air and transition metal-free conditions was described. The protocol has advantages of using relatively cheap, less toxic, stable and easy-to-handle cyanating reagent, high yield, and mild reaction condi- tion.
基金National Natural Science Foundation of China,Grant/Award Number:62171023China Postdoctoral Science Foundation,Grant/Award Number:2021TQ0165。
文摘Calculated parameters(soil layer resistivity,soil layer thickness,and the number of soil layers)of horizontally layered soil are usually obtained based on the measured apparent resistivity under different measurement distances,which are significant for the design,operation,and maintenance of grounding grids.Most existing calculation methods of soil parameters are just trying to make the calculation results approach the measurement data,ignoring the relationship among measurement data,the calculated soil parameters,and grounding parameters,which would increase the workload of the measurement.To better balance the distance range of the measurement data and the influence of the calculated horizontally layered soil on grounding parameters,this paper systematically studies the relationship among measured apparent soil resistivity,calculated horizontally layered soil parameters,and grounding parameters.The basic theories of apparent resistivity measurement,soil parameter calculation,and grounding parameter calculation are given,the influence of soil layer thickness on the measured apparent resistivity is studied,and the influence of the calculated soil parameters on the grounding resistance of different grounding models is analysed.Based on different scales of grounding grids,the results give a corresponding reference distance range of measured apparent soil resistivity.Therefore,this paper can help decrease the workload of soil resistivity measurement during grounding parameters analysis,which has far-reaching engineering significance.
