Interferometry with computer-generated holograms(CGHs)is a unique solution for the highly accurate testing of large-aperture aspheric mirrors.However,no direct testing method for quantifying the measurement accuracy o...Interferometry with computer-generated holograms(CGHs)is a unique solution for the highly accurate testing of large-aperture aspheric mirrors.However,no direct testing method for quantifying the measurement accuracy of CGHs has been developed.In this study,we developed a methodology for verifying CGH accuracy based on an element that is functionally equivalent to a large-aperture mirror in terms of accuracy verification.The equivalent element decreased the aperture by one or higher orders of magnitude,implying that the mirror could be replaced by a non-CGH technology in a comparison test.In this study,a 281 mm diamond-turned mirror was fabricated as the equivalent element of a 3.5 m aspheric mirror and measured using CGH and LUPHOScan profilometers.Surface error composition and root-mean-square(RMS)density analyses were performed.The methodology verification accuracy of the CGH was 4 nm(RMS)in the low-to mid-frequency bands,with a measured surface accuracy of approximately 10 nm(RMS).This methodology provides a feasible solution for CGH accuracy verification,ensuring high-accuracy and reliable testing of large-aperture aspheric mirrors.展开更多
The accurate and reliable interpretation of regional land cover data is very important for natural resource monitoring and environmental assessment.At present,refined land cover data are mainly obtained by manual visu...The accurate and reliable interpretation of regional land cover data is very important for natural resource monitoring and environmental assessment.At present,refined land cover data are mainly obtained by manual visual interpretation,which has the problems of heavy workload and inconsistent interpretation scales.Deep learning has greatly improved the automatic processing and analysis of remote sensing data.However,the accurate interpretation of feature information from massive datasets remains a difficult problem in wide regional land cover classification.To improve the efficiency of deep learning-based remote sensing image interpretation,we selected multisource remote sensing data,assessed the interpretability of the U-Net model based on surface spatial scenes with different levels of complexity,and proposed a new method of stereoscopic accuracy verification(SAV)to evaluate the reliability of the classification result.The results show that classification accuracy is more highly correlated with terrain and landscape than with other factors related to image data,such as platform and spatial resolution.As the complexity of surface spatial scenes increases,the accuracy of the classification results mainly shows a fluctuating declining trend.We also find the distribution characteristics from the SAV evaluation results of different land cover types in each surface spatial scene.Based on the results observed in this study,we consider the distinction of interpretability and reliability in diverse ground object types and design targeted classification strategies for different surface scenes,which can greatly improve the classification efficiency.The key achievement of this study is to provide the theoretical basis for remote sensing information analysis and an accuracy evaluation method for regional land cover classification,and the proposed method can help improve the likelihood that intelligent interpretation can replace manual acquisition.展开更多
After being launched into orbit,the geometric calibration of a satellite laser altimeter will reduce errors in laser pointing and ranging caused by satellite vibrations during launch,environmental changes,and thermal ...After being launched into orbit,the geometric calibration of a satellite laser altimeter will reduce errors in laser pointing and ranging caused by satellite vibrations during launch,environmental changes,and thermal effects during long-term operation,which guarantees the accuracy of measurement data.In this study,a satellite laser geometric calibration method combining infrared detectors and corner-cube retroreflectors(CCRs)is proposed.First,a CCR-based laser ranging error calibration method was established,and then a laser pointing error calibration model was derived based on a single infrared detector array.Taking GaoFen-7(GF-7)satellite laser beam 2 as the experimental object,laser geometric calibration was realized using an infrared detector and CCR-measured data.Then,the accuracy of the proposed method was compared with that of other calibration methods,the CMLID and the CMSPR.The results show that the accuracy of the proposed calibration method is equivalent to that of the CMLID and higher than that of the CMSPR.Among them,the accuracy of the laser pointing after calibration using the proposed method is better than 0.8 arcsec,and the elevation accuracy of the laser on flat,sloping,and mountainous terrains is better than 0.11 m,0.30 m,and 1.80 m,respectively.展开更多
基金supported by the National Natural Science Foundation of China(62127901,52375471,and 62305333)supported by the National Key Research and Development Program(2022YFB3403405)+2 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences(2019221)Young Elite Scientists Sponsorship Program of CAST(2022QNRC001)Young Elite Scientists Sponsorship Program of Jilin Province(QT202222).
文摘Interferometry with computer-generated holograms(CGHs)is a unique solution for the highly accurate testing of large-aperture aspheric mirrors.However,no direct testing method for quantifying the measurement accuracy of CGHs has been developed.In this study,we developed a methodology for verifying CGH accuracy based on an element that is functionally equivalent to a large-aperture mirror in terms of accuracy verification.The equivalent element decreased the aperture by one or higher orders of magnitude,implying that the mirror could be replaced by a non-CGH technology in a comparison test.In this study,a 281 mm diamond-turned mirror was fabricated as the equivalent element of a 3.5 m aspheric mirror and measured using CGH and LUPHOScan profilometers.Surface error composition and root-mean-square(RMS)density analyses were performed.The methodology verification accuracy of the CGH was 4 nm(RMS)in the low-to mid-frequency bands,with a measured surface accuracy of approximately 10 nm(RMS).This methodology provides a feasible solution for CGH accuracy verification,ensuring high-accuracy and reliable testing of large-aperture aspheric mirrors.
基金Under the auspices of National Natural Science Foundation of China(No.41971352)Key Research and Development Project of Shaanxi Province(No.2022ZDLSF06-01)。
文摘The accurate and reliable interpretation of regional land cover data is very important for natural resource monitoring and environmental assessment.At present,refined land cover data are mainly obtained by manual visual interpretation,which has the problems of heavy workload and inconsistent interpretation scales.Deep learning has greatly improved the automatic processing and analysis of remote sensing data.However,the accurate interpretation of feature information from massive datasets remains a difficult problem in wide regional land cover classification.To improve the efficiency of deep learning-based remote sensing image interpretation,we selected multisource remote sensing data,assessed the interpretability of the U-Net model based on surface spatial scenes with different levels of complexity,and proposed a new method of stereoscopic accuracy verification(SAV)to evaluate the reliability of the classification result.The results show that classification accuracy is more highly correlated with terrain and landscape than with other factors related to image data,such as platform and spatial resolution.As the complexity of surface spatial scenes increases,the accuracy of the classification results mainly shows a fluctuating declining trend.We also find the distribution characteristics from the SAV evaluation results of different land cover types in each surface spatial scene.Based on the results observed in this study,we consider the distinction of interpretability and reliability in diverse ground object types and design targeted classification strategies for different surface scenes,which can greatly improve the classification efficiency.The key achievement of this study is to provide the theoretical basis for remote sensing information analysis and an accuracy evaluation method for regional land cover classification,and the proposed method can help improve the likelihood that intelligent interpretation can replace manual acquisition.
基金supported by National Key Research and Development Program of China:[Grant Number 2020YFE0200800]National Natural Science Foundation of China:[Grant Number 41971426]+1 种基金Special Funds for Creative Research:[Grant Number 2022C61540]Innovative Youth Talents Program,Ministry of Natural Resources of the People’s Republic of China:[Grant Number 12110600000018003930].
文摘After being launched into orbit,the geometric calibration of a satellite laser altimeter will reduce errors in laser pointing and ranging caused by satellite vibrations during launch,environmental changes,and thermal effects during long-term operation,which guarantees the accuracy of measurement data.In this study,a satellite laser geometric calibration method combining infrared detectors and corner-cube retroreflectors(CCRs)is proposed.First,a CCR-based laser ranging error calibration method was established,and then a laser pointing error calibration model was derived based on a single infrared detector array.Taking GaoFen-7(GF-7)satellite laser beam 2 as the experimental object,laser geometric calibration was realized using an infrared detector and CCR-measured data.Then,the accuracy of the proposed method was compared with that of other calibration methods,the CMLID and the CMSPR.The results show that the accuracy of the proposed calibration method is equivalent to that of the CMLID and higher than that of the CMSPR.Among them,the accuracy of the laser pointing after calibration using the proposed method is better than 0.8 arcsec,and the elevation accuracy of the laser on flat,sloping,and mountainous terrains is better than 0.11 m,0.30 m,and 1.80 m,respectively.
