Horn Antenna has many applications such as communication, radar, and standard reference antenna for measurement. In this research, we designed a pyramidal horn for a Circularly Polarized Synthetic Aperture Radar (CP-S...Horn Antenna has many applications such as communication, radar, and standard reference antenna for measurement. In this research, we designed a pyramidal horn for a Circularly Polarized Synthetic Aperture Radar (CP-SAR) sensor onboard a microsatellite. We utilized a 3D printer with Fused Deposition Modelling (FDM) technology for fast, low-cost, and low-weight production. Polylactide (PLA) material was used to construct 3D structures, and a copper conductive coating was painted on its surface. Gaussian distribution function was employed to create a septum polarizer profile. NPC-220 A with 1.6 thickness and 2.17 dielectric constant was used to make a microstrip monopole antenna and stripline feeding to feed the pyramidal horn to generate TE01 mode at one side of the waveguide. The design, parametric studies, and measurements are discussed in this paper. The designed antenna can achieve wide bandwidth 28% of 3 dB axial ratio, and more than 22% of s11 ≤ −10 dB in working frequency that is acceptable for CP-SAR requirement on the microsatellite.展开更多
We aim to directly invert wave parameters by using the data of a compact polarimetric synthetic aperture radar(CP SAR)and validate the effectiveness of ocean wave parameter retrieval from the circular transmit/linear ...We aim to directly invert wave parameters by using the data of a compact polarimetric synthetic aperture radar(CP SAR)and validate the effectiveness of ocean wave parameter retrieval from the circular transmit/linear receive mode andπ/4 compact polarimetric mode.Relevant data from the RADARSAT-2 fully polarimetric SAR on the C-band were used to obtain the compact polarimetric SAR images,and a polarimetric SAR wave retrieval algorithm was used to verify the sea surface wave measurements.Using the data and algorithm,there is no need to estimate complex hydrodynamic modulation transfer functions,even at large radar incidence angles.First,the radar backscattering cross-sections and backscattering cross-section of the radar linearly polarized with any polarization orientation angle were calculated in the two compact polarimetric SAR modes.Then,the wave slopes along the azimuth direction and the range direction were calculated directly using CP SAR data.Finally,we obtained the slope spectrum of the wave from the estimated wave slopes along azimuth and range directions.The wave parameters extracted from the synthetic wave slope spectrum were compared with those obtained from buoy observations of the National Data Buoy Center,verifying a suitable agreement.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
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
文摘Horn Antenna has many applications such as communication, radar, and standard reference antenna for measurement. In this research, we designed a pyramidal horn for a Circularly Polarized Synthetic Aperture Radar (CP-SAR) sensor onboard a microsatellite. We utilized a 3D printer with Fused Deposition Modelling (FDM) technology for fast, low-cost, and low-weight production. Polylactide (PLA) material was used to construct 3D structures, and a copper conductive coating was painted on its surface. Gaussian distribution function was employed to create a septum polarizer profile. NPC-220 A with 1.6 thickness and 2.17 dielectric constant was used to make a microstrip monopole antenna and stripline feeding to feed the pyramidal horn to generate TE01 mode at one side of the waveguide. The design, parametric studies, and measurements are discussed in this paper. The designed antenna can achieve wide bandwidth 28% of 3 dB axial ratio, and more than 22% of s11 ≤ −10 dB in working frequency that is acceptable for CP-SAR requirement on the microsatellite.
基金The National Natural Science Foundation of China under contract Nos 41620104003 and 42027805the National Key Research and Development Program under contract No.2016YFC1401002。
文摘We aim to directly invert wave parameters by using the data of a compact polarimetric synthetic aperture radar(CP SAR)and validate the effectiveness of ocean wave parameter retrieval from the circular transmit/linear receive mode andπ/4 compact polarimetric mode.Relevant data from the RADARSAT-2 fully polarimetric SAR on the C-band were used to obtain the compact polarimetric SAR images,and a polarimetric SAR wave retrieval algorithm was used to verify the sea surface wave measurements.Using the data and algorithm,there is no need to estimate complex hydrodynamic modulation transfer functions,even at large radar incidence angles.First,the radar backscattering cross-sections and backscattering cross-section of the radar linearly polarized with any polarization orientation angle were calculated in the two compact polarimetric SAR modes.Then,the wave slopes along the azimuth direction and the range direction were calculated directly using CP SAR data.Finally,we obtained the slope spectrum of the wave from the estimated wave slopes along azimuth and range directions.The wave parameters extracted from the synthetic wave slope spectrum were compared with those obtained from buoy observations of the National Data Buoy Center,verifying a suitable agreement.
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.
