Research On Registration And Stitching Method Of Remote Sensing Image

Remote sensing image registration and stitching technology are important parts of remote sensing image processing.It has been widely used in many fields such as national defense military security,natural disaster analysis,agricultural and forestry resources investigation.urban rational planning and so on.However,due to the complex imaging mechanism and imaging conditions of remote sensing images,the traditional image registration algorithms are cumbersome to extract features and prone to mismatch the feature points,which directly affects the stitching quality of remote sensing images.Therefore,from the perspective of spatial parameter prediction and optimal stitching,the:thesis deeply studies the registration and splicing of remote sensing images,and then gives a remote sensing image registration algorithm based on convolutional neural network and correction network At the same time,the energy function stitching algorithm based on visual perception is applied to the image stitching process.In the thesis,a remote sensing image registration algorithm combining convolutional neural network and correction network is used in the registration process of remote sensing images.The convolutional neural network is used to directly predict the transformation parameters between images,and then combined with the correction,network to find the best transformation parameter.First,the algorithm uses an affine transformation network to affine transform the reference image to generate training samples in batches Secondly,the algorithm puts feature extraction and feature matching in the end-to-end architecture of the convolutional neural network,learns the deformation model between the reference image and the floating image,and directly predicts the affine transformation parameters between the images Finally, the algorithm uses correction network to correct the prediction results of the convolutional neural network to achieve more accurate registration of remote sensing images Expenmental results show that compared with the commonly used SIFT algorithm,SLRF algorithm and deep neural network algorithm,the algorithm in this thesis has significantly improved the accuracy and speed of image registration.In order to make the remote-sensing image have a better visual effect after stitching,the paper uses an energy function stitching algorithm based on visual perception to convert the non-linearity and saliency of human vision into a function with the smallest energy value.The sigmoid function is used to simulate the perception of visual chromatic aberration and the weight of the saliency target is adjusted to obtain the visual most between the images to be stitched.Finally algorithm in the thesis uses the weighted average method of multi-region gradual in and out to carry out image fusion stitching.The experimental results show that the image stitching results of the algorithm in the thesis is more informative and visually better than the color difference stitching algorithm and the color gradient stitching algorithm.