Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-...Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.展开更多
The operational cloud-motion tracking technique fails to retrieve atmospheric motion vectors （AMVs） in areas lacking cloud; and while water vapor shown in water vapor imagery can be used, the heights assigned to the...The operational cloud-motion tracking technique fails to retrieve atmospheric motion vectors （AMVs） in areas lacking cloud; and while water vapor shown in water vapor imagery can be used, the heights assigned to the retrieved AMVs are mostly in the upper troposphere. As the noise-equivalent temperature difference （NEdT） performance of FY-2E split win- dow （10.3-11.5 μm, 11.6-12.8 μm） channels has been improved, the weak signals representing the spatial texture of water vapor and aerosols in cloud-free areas can be strengthened with algorithms based on the difference principle, and applied in calculating AMVs in the lower troposphere. This paper is a preliminary summary for this purpose, in which the principles and algorithm schemes for the temporal difference, split window difference and second-order difference （SD） methods are introduced. Results from simulation and cases experiments are reported in order to verify and evaluate the methods, based on comparison among retrievals and the ＂truth＂. The results show that all three algorithms, though not perfect in some cases, generally work well. Moreover, the SD method appears to be the best in suppressing the surface temperature influence and clarifying the spatial texture of water vapor and aerosols. The accuracy with respect to NCEP 800 hPa reanalysis data was found to be acceptable, as compared with the accuracy of the cloud motion vectors.展开更多
基金supported by the National Natural Science Foundation of China(61172127)the Natural Science Foundation of Anhui Province(1408085MF121)
文摘Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.
基金supported by the National Natural Science Foundation of China(Grant Nos.41175035 and 40475018)the National Basic Research Program of China(Grant No.2009CB421502)a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘The operational cloud-motion tracking technique fails to retrieve atmospheric motion vectors （AMVs） in areas lacking cloud; and while water vapor shown in water vapor imagery can be used, the heights assigned to the retrieved AMVs are mostly in the upper troposphere. As the noise-equivalent temperature difference （NEdT） performance of FY-2E split win- dow （10.3-11.5 μm, 11.6-12.8 μm） channels has been improved, the weak signals representing the spatial texture of water vapor and aerosols in cloud-free areas can be strengthened with algorithms based on the difference principle, and applied in calculating AMVs in the lower troposphere. This paper is a preliminary summary for this purpose, in which the principles and algorithm schemes for the temporal difference, split window difference and second-order difference （SD） methods are introduced. Results from simulation and cases experiments are reported in order to verify and evaluate the methods, based on comparison among retrievals and the ＂truth＂. The results show that all three algorithms, though not perfect in some cases, generally work well. Moreover, the SD method appears to be the best in suppressing the surface temperature influence and clarifying the spatial texture of water vapor and aerosols. The accuracy with respect to NCEP 800 hPa reanalysis data was found to be acceptable, as compared with the accuracy of the cloud motion vectors.
