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This paper takes the advantageous ability of Kalman filter equation as a means to jointly realize the accurate and reliable extraction of 3D spatial information and carries out the research work from the extraction of 3D spatial position information from multisource remote sensing optical stereo image pairs, recovery of 3D spatial structure information, and joint extraction of 3D spatial information with optimal topological structure constraints, respectively. Taking advantage of the stronger effect capability of Wiener recovery and shorter computation time of Kalman filter recovery, Wiener recovery is combined with Kalman filter recovery (referred to as Wiener-Kalman filter recovery method), and the mean square error and peak signal-to-noise ratio of the recovered image of this method are comparable to those of Wiener recovery, but the subjective evaluation concludes that the recovered image obtained by the Wiener-Kalman filter recovery method is clearer. To address the problem that the Kalman filter recovery method has the advantage of short computation time but the recovery effect is not as good as the Wiener recovery method, an improved Kalman filter recovery algorithm is proposed, which overcomes the fact that the Kalman filter recovery only targets the rows and columns of the image matrix for noise reduction and cannot utilize the pixel point information among the neighboring rows and columns. The algorithm takes the first row of the matrix image as the initial parameter of the Kalman filter prediction equation and then takes the first row of the recovered image as the initial parameter of the second Kalman filter prediction equation. The algorithm does not need to estimate the degradation function of the degradation system based on the degraded image, and the recovered image presents the image edge detail information more clearly, while the recovery effect is comparable to that of the Wiener recovery and Wiener-Kalman filter recovery method, and the improved Kalman filter recovery method has stronger noise reduction ability compared with the Kalman filter recovery method. The problem that the remote sensing optical images are seriously affected by shadows and complex environment detail information when 3D spatial structure information is extracted and the data extraction feature edge is not precise enough and the structure information extraction is not stable enough is addressed. A global optimal planar segmentation method with graded energy minimization is proposed, which can realize the accurate and stable extraction of the topological structure of the top surface by combining the edge information of remote sensing optical images and ensure the accuracy and stability of the final extracted 3D spatial information.

Optimized Design of 3D Spatial Images Based on Kalman Filter Equation