The theoretical model of atmospheric refractive index based on the standard atmospheric environment cannot explain the atmospheric refractive index when optical satellites detect targets. Aiming at this problem, a method for estimating the refractive index of the atmosphere based on multispectral stellar observation data is proposed. Based on optical satellites' multispectral stellar observation data, according to the principle of deflection when the stellar light passes through the atmosphere, the optical path model of the stellar light refracted by the atmosphere is established under the assumption of a layered spherical atmosphere. A method of using multispectral segment stars to measure the actual light and the iterative forward feedback of stellar theoretical light is proposed, to estimate the refractive index of each layer of the atmosphere of the layered atmosphere to different spectral segments of light.
Target detection of arbitrary shape is widely used in remote sensing image processing, The case segmentation method based on contour regression is very similar to the target detection method. At present, many scholars have studied case segmentation using deep learning framework, case segmentation based on contour regression can be regarded as a simple extension of object detection, it is a more accurate target detection. In this paper, the contour regression method based on Fourier operator is used for accurate target detection, the Fourier feature vectors in a group of frequencies are predicted at each position of the feature map, and then the target contour point sequence is reconstructed in the image space domain through inverse Fourier transform, The simulation results show that the detection accuracy of the proposed method is more than 10% higher than that of the classical method.
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