Multi-image joint registration method based on least square estimation
A least squares method and least squares technology, applied in the field of signal processing, can solve the problems of poor registration accuracy, low registration accuracy of auxiliary images, and no consideration of auxiliary images, etc., to achieve the effect of improving registration accuracy
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Embodiment 1
[0026] The commonly used multi-image registration method in radar interferometric processing is SMC, but this method is greatly affected by temporal and / or spatial decoherence and error propagation effects, resulting in low registration accuracy. When the radar platform scans the same scene area on the ground multiple times, it will form multiple SAR images for this scene area. Due to the different motion trajectories of the observation radar platform, the images formed by multiple observations of the same area target will drift in the same resolution unit. Scaling or rotation effects cause small pixel deviations in the distance and azimuth directions of these images, and the phase difference cannot reflect the height fluctuation of the ground, so it is naturally impossible to invert the DEM of the scene area. Therefore, the interferometric multi-image registration process must be performed to match multiple SAR images well, and the pixels at the corresponding positions corresp...
Embodiment 2
[0036] The multi-image joint registration method based on least squares estimation is the same as embodiment 1, and connects all SAR images with the Delaunay triangulation method described in step (1), including the following steps:
[0037] (1a) In the space baseline-time baseline two-dimensional plane, use the Delaunay triangulation method to connect all the SAR images in the image set.
[0038] (1b) Keep all SAR images in the image set within a single Delaunay triangulation.
[0039] (1c) Optimize the network by threshold comparison: set appropriate spatial and temporal baseline thresholds, use the threshold comparison method, discard arcs with longer spatial and / or temporal baselines to optimize the Delaunay triangulation, and retain as much coherence as possible Stronger image pairs.
[0040] After connecting all SAR images with optimized Delaunay triangulation, image pairs with strong coherence can be kept as much as possible, while image pairs with longer space and / or ...
Embodiment 3
[0042] The multi-image joint registration method based on least squares estimation is the same as embodiment 1-2. In step (2), it is estimated that the registration offset of the SAR image pair after optimizing the Delaunay triangulation network connection includes the following steps:
[0043] (2a) Define the registration offset of the connected image pairs: Suppose there are M SAR image pairs connected by the optimized Delaunay triangulation, then the registration offsets of the M connected image pairs in the distance and azimuth directions are expressed as :
[0044] δa=[δa 1 ,…,δa M ] T (1)
[0045] δr=[δr 1 ,…,δr M ] T (2)
[0046] Among them, δa 1 ,…,δa M is the registration offset of the M connected image pairs in the azimuth direction, which is stored in the vector δa; δr 1 ,…,δr M is the registration offset of the M connected image pairs in the upward distance, which is stored in the vector δr; the superscript T represents the transposition of the vector....
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