Chromatographic SAR three-dimensional point cloud generation method based on generalized orthogonal matching pursuit
An orthogonal matching tracking and three-dimensional point cloud technology, which is applied in the directions of radio wave reflection/re-radiation, re-radiation, and measurement devices, can solve problems such as difficult and effective imaging, uneven distribution, and limited number of tracks , to achieve the effect of reducing complexity, simplifying computational complexity, and ensuring the robustness of reconstruction
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Embodiment 1
[0035] figure 1 and figure 2 A tomographic SAR urban building three-dimensional point cloud generation method is shown, comprising the following steps:
[0036] S1. Obtain multi-temporal SAR image data of the target building;
[0037] S2. Focusing in the height dimension (vertical slant distance) through a compressed sensing algorithm based on generalized orthogonal matching pursuit;
[0038] S3. Obtain the elevation information of the target, encrypt the point cloud by interpolation, express and output the reconstructed target.
[0039] Preferably, the step S1 specifically includes: acquiring N-scene two-dimensional SAR image data of the same target at different times and different track positions by the sensor.
[0040] Preferably, the two-dimensional SAR image data includes orbit altitude, revisit period, incident angle range, azimuth resolution, distance resolution, single-channel scene size, and maximum acquisition length.
[0041] As preferably, said step S2 specifi...
Embodiment 2
[0064] Select 14 scenes of ascending orbit images under the "TerraSAR-X" HH polarization mode in the Beijing area from 2011 to 2013 as the experimental data. 3.3m, and take Beijing Pangu Seven Star Hotel as an example object. In the example, the image in December 2011 is the main image, and the other images are the secondary images. After SAR image registration, single building selection and cropping, image de-skewing and phase error compensation and other data preprocessing, the example building is realized based on the generalized orthogonal matching tracking algorithm according to the steps described in the above-mentioned part 5. 3D imaging, the results are as follows Figure 4 As shown, the point cloud after interpolation and encryption is as follows Figure 5 shown.
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