Comprehensive Evaluation And Adaptive Restoration Of Remotely Sensed Images

Due to the disturbances of various factors in the acquisition process, the remotely sensed images may have lower quality, which brings a good many difficulties for the subsequent applications. In order to relieve this problem, it is needed to evaluate the image quality to select remote sensing data with better quality, and to filter the poor-quality images. On the other hand, it is needed to restore the images for potential improvement in the condition of no high-quality images existing. Therefore, image evaluation and image restoration are important steps in remote sensing information processing and application.Although the problems of image evaluation and image restoration have obtained wide attentions in the academic and application fields, the existing methods can not solve the difficulties perfectly. How to consider multiple factors for a comprehensive evaluation and how to consider the features of different images for an adaptive restoration, are the main difficulties to be solved. This thesis aims to these problems, and develop theories and methods for the evaluation and restoration of remote sensing images. The main contents are as follows:(1) The current evaluation methods of noise and Modulation Transfer Function (MTF) always have low degree of automation, and need manual intervention. Aiming at this problem, this thesis proposes automatic selection methods of the optimal evaluation regions. As for the noise assessment, an iterative optimization method for the selection of homogeneous area is proposed. This method may solve the difficulties in traditional methods and realize robust noise estimation by using convolution based evaluation index. As for the MTF assessment, in order to solve the problem of knife edge searching in MTF computing, an automatic searching and calculating method is presented. The experimental results show that the automatic evaluation methods are accurate and effective, and can greatly improve the degree of automation of remote sensing noise and MTF evaluation.(2) This thesis gives a systematically summary of the current image evaluation indices, and makes several improvements. Base on these, a comprehensive evaluation method for remote sensing images is proposed by fully consider the indices of grey distribution, information quantity, definition, resolution, noise, cloud, and invalid pixels. The reference based method is also considered. After a plenary test of remote sensing data, the optimal thresholds of different grades (Excellent, Good, Fair and Poor) are determined. In the framework of fuzzy mathematics theory, the fuzzy evaluation matrix is firstly built, and the results of the evaluation of a variety of single indicators are considered comprehensively, then a overall qualitative evaluation score is given. The experimental results show that this comprehensive evaluation method can accurately evaluate the quality of remote sensing images with good consistency with the human eye evaluation.(3) This thesis proposes an adaptive non-local regularization denoising method for remotely sensed images. Traditional denoising methods only use the neighboring information, leading to that the noise can not be effectively removed. This research develop new denoising method in the framework of non-local computation, and solve the problem of adaptive selection of filtering parameters. In this thesis, based on the non-local total variation denoising model, an adaptive filtering parameter determination approach is presented by computing the local noise intensity and standard deviation. In the iterative process, the parameters are adjusted automatically according to the noise level of the iteration results. Experimental results show that this filtering parameter determination method have the performance to adaptively adjusted the denoising strength according to different structures and can protect the edge and texture information while removing image noise.(4) This thesis proposes an adaptively alternative iteration method for blind deblurring of remotely sensed images. This method is based on the framework of maximum a posteriori (MAP), and jointly solves the image and blur function by alternative iteration procedure. During the iteration process, make full use of the partially solved image to compute the values of data consistency and regularization terms. The two regularization parameters of image and blur are adaptively solved by designing corresponding solution functions. The experimental results show that the proposed blind deblurring method has the performance to give accurate estimation of blurring function, and realize adaptive restoration according to image features. It can improve the processing accuracy and efficiency.