Removing atmospheric MTF and establishing an MTF compensation filter for the HJ-1A CCD camera
Source: International Journal of Remote Sensing, Volume 34, Number 4, 2013 , pp. 1413-1427(15)
Publisher: Taylor and Francis Ltd
Abstract:The total image modulation transfer function (MTF) comprises imaging system MTF and atmospheric MTF, both of which can produce blurring effects in remote-sensing images. Imaging system MTF, one of the important specifications of the imaging system, is used to characterize the radiometric response of the sensor. Atmospheric MTF, which consists of aerosol MTF and turbulence MTF, usually changes with external atmospheric conditions. To obtain the MTF of the imaging system, the atmospheric MTF has to be removed from the total image MTF. This study aims to determine a method for removing the atmospheric MTF for the charge-coupled device (CCD) camera on-board the Huanjing 1A satellite (HJ-1A). The aerosol MTF and the turbulence MTF in the CCD images were first predicted based on the aerosol optical depth (AOD) derived by dark target and reanalysis of data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR). Then, the MTFs of the CCD camera were retrieved by removing the aerosol and turbulence MTFs from the total on-orbit image MTFs that were calculated from the linear features on CCD images. For the HJ-1A CCD camera, results show that the MTF values at Nyquist frequency increased by about 10% after the atmospheric MTFs were removed. The total image MTF became degraded by about 50% when AOD was high or turbulence was strong. Furthermore, this study has established an MTF compensation (MTFC) filter for the HJ-1A by modifying the ‘modified inverse filter (MIF)’ approach. Results illustrate that the effective instantaneous field of view (EIFOV) of the restored image improved from 78 to 41 m, while the noise level of the model was well controlled. This research demonstrates that the modified approach is effective for sensors with low MTF values.
Document Type: Research Article
Affiliations: 1: State Key Laboratory of Remote Sensing Science,Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing,100101, China 2: China Centre for Resources Satellite Data and Application, Beijing,100094, China
Publication date: February 20, 2013