For two linear / three linear array mapping cameras, the on-orbit angle change between mapping cameras / star earth cameras is the key to affect the accuracy of mapping / no control point positioning. In this paper, the on-orbit optical axis pointing change of a high-precision stereo mapping camera is evaluated, which is based on the on-orbit monitor system of boresight angle change between front and rear view camera/star cameras. Firstly, the spot image quality evaluation of the boresight position recorder of star earth cameras is carried out. It mainly includes shape, gray value, noise and so on. Secondly, the optical axis pointing stability of star earth cameras is analyzed. Finally, the optical axis pointing accuracy is analyzed. The results show that the performance of boresight position recorder of star earth cameras is good after on-orbit parameter optimization, the boresight pointing accuracy of star earth cameras is better than 0.2″, focus accuracy is better than 0.005mm. It provides a good reference for the follow-up project promotion.
At present, the accuracy of star sensor calibration based on real space is limited by the factors of vibration, atmospheric environment and so on. In this paper, a new calibration method for digital Time Delay Integrate (TDI) star camera is proposed based on two star cameras joint observation, overcoming the disadvantage mentioned above with high accuracy. Firstly, a mathematic modeling process is introduced with the proposed method. Then an experimental system is set up with two star cameras and an equatorial. Finally, static and dynamic measuring is complemented and errors are analyzed. The results show that, with the proposed method the calibration accuracies of star camera round X/Y axes are 1.45″ (3σ)/0.84″ (3σ), respectively.
KEYWORDS: Image quality, Image compression, Image processing, Remote sensing, Principal component analysis, Algorithm development, Signal to noise ratio
Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling,which can improve efficiency. A method of two-dimensional principal component analysis(2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.
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