Error Modeling And Image Quality Improving Method In Optical Remote Sensing Imaging

Optical remote sensing imaging is of great importance when dealing with the air-based reconnaissance, surveillance and mapping in the military. Although China has owned several optical imaging satellites for earth observation and the basic ability of above military application, the quality of images obtained by our satellites is extremely lower compared with the satllites of foreign with equal resolution, which affects the application efficiency of images. Literature research showed that the main problems for quality degration were the errors from the imaging process. Thus, how to model errors, identify errors and suppress errors in imaging becomes the vital problem to improve the quality of the image and the application efficiency of the imaging system.This paper focuses on the problem of image quality improving. In this paper, the problem is divided into two parts, specifically, error modeling in foreward-process and error identifying in backward-process. By combining the error modeling in foreward-process and the error identification in backward-process, this paper studies some methods to improve the quality of image, such as image restoration, image interpolation and image spectrum expanding.The main innovations are as follows.Firstly, a general Gaussian model of imaging error is established by adapting the physical information and priori information of the imaging process, which can improve the identification probability and reduce the complexity of the imaging model. Theory analysis and numerical experiments demonstrate the efficiency of the model. By combining the general Gaussian model and the sparse representation of image, a minimal functional model is extablished to identify the degraded function of the imaging system. Meanwhile, a fuzzy inferential model is established to suppress the pepper noise of the image. An algorithm based on the cross-direction iteration is designed to estimate the parameters in model. Numerical results demonstrate the efficiency of this method.Secondly, to suppress the effect of error from satellite and camera on image, a method based on the spectrum and cepstrum is established to identify the motion error of satellite, and a new two-step distortion calibration model based on the invariability of inter-star angle are developed to suppress the effect of attitude error of satellite. The model based on the spectrum and cepstrum can indentify complex motion error with sine oscillation and even-velocity motion. The calibration model of star tracker camera can estimate the camera parameters even with higher order nonlinear distortions based on single image. Furthermore, a compact recursive average filter is designed to improve the accuracy without adding significantly computation time.Thirdly, for the problem of image restoration, this paper adapts the merits on edge direction representation of dual-tree complex wavelet, and establishes the regularization model of image restoration based on the sparsity of image representation on complex wavelet domain. This model is also generalized to the image restoration problem with spatial varied degraded function. Furthermore, to solve the problem of high computational complexity in remote sensing, a cascadic multigrid algorithm is provided. This new algorithm can save 50% computation time without changing the solution accuracy.Finally, to improve the image quality, this paper studies some new methods of image inpainting, image interpolation and image spectrum expanding under the frame of compressive sensing. In this paper, the three problems are turned to the problems of image reconstruction in compressive sensing and the reconstructed algorithms are also put out. Furthermore, to improve the performance of projected measurement matrix, a regularization-based method is put out. By designing suitable numerical experiments, the efficiency of models and algorithms is also analyzed, and the results show that these new methods can adapt the sparsity of the images and have better performance than the known methods, and they can also dispose the problems with noise.In conclusion, this paper establishes the error model of the main pocess in optical remote sensing imaging, introduces a new method for error identifying based on image, and obtains a serie of technique, such as image restoration, image interpolation, image impainting and image spectral expanding, to improve the quality of image. These results can be applied to improve the image quality of our imaging satellite in orbit, and they can also be used to support the optimal design of future imaging system.