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- W2988390284 abstract "Attitude jitter of observation satellites is a problem that degenerates the imaging quality in high-resolution (HR) remote sensing. In order to retrieve jitters with high accuracy, a new way to fuse image registration and convolutional neural network by a Kalman filter is described. In this method, a novel and complete framework based on image registration are proposed to estimate the on-orbit attitude variations from multi-spectrum remote sensing images. Then a convolutional neural network(CNN) is proposed to estimate the attitude jitter by automatically learning essential scene features from a single image. A Kalman filter is derived to fuse the jitter obtained from the two algorithms. The proposed methodology is trained and tested with PatternNet dataset. Simulation results show that the attitude variations are detected with high accuracy and the deformed images are compensated." @default.
- W2988390284 created "2019-11-22" @default.
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- W2988390284 date "2019-07-01" @default.
- W2988390284 modified "2023-10-07" @default.
- W2988390284 title "Remote Sensing Satellite Jitter Detection Based on Image Registration and Convolutional Neural Network Fusion" @default.
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- W2988390284 doi "https://doi.org/10.1109/igarss.2019.8899150" @default.
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