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| Citation: | Wang Song, Zhang Fubo, Chen Longyong, Liang Xingdong. Array-interferometric Synthetic Aperture Radar Point Cloud Filtering Based on Spatial Clustering Seed Growth Algorithm[J]. Journal of Radars, 2018, 7(3): 355-363. doi: 10.12000/JR18006
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Array-interferometric Synthetic Aperture Radar Point Cloud Filtering Based on Spatial Clustering Seed Growth Algorithm
doi: 10.12000/JR18006
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Abstract
By arranging multiple antennas in the intersection direction and combining the synthetic aperture of azimuth direction and large bandwidth signal with oblique distance, array-interferometric Synthetic Aperture Radar (SAR) can generate a three-dimensional resolution and ensure the elevation spacial sampling due to its multiple array element, which could avoid the layover problem in surveying and mapping in the Interference SAR (InSAR) and realize the three-dimensional imaging of the observation scene. However, considering the existence of too much noise in the three-dimensional point cloud distribution in the scene area and the large elevation error, the traditional Light Detection And Ranging (LiDAR) point cloud filtering method is not suitable for the filtering processing of the array-interferometric SAR point cloud. In order to solve this problem, an array-interferometric SAR point cloud filtering algorithm based on spatial clustering seed growth algorithm is proposed, in which the density-elevation image is generated by the double threshold of density and elevation, the small clutter is removed by image processing, and the vegetation is removed from the point cloud data by using the spatial clustering seed growth algorithm, thus the point cloud filtering process is completed. Using the first airborne array-interferometric SAR experimental data, the validity of the proposed algorithm is verified compared to the traditional LiDAR filtering method, which provides the guarantee for the subsequent building extraction and meticulous treatment. -
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References
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- Figure 1. The original three-dimensional point cloud
- Figure 2. Simple building model
- Figure 3. The encoding of the neighborhood in the search
- Figure 4. Flowchart of seed growth
- Figure 5. Schematic diagram before and after cloud processing
- Figure 6. Local enlarged image after processing
- Figure 7. Density-elevation image before and after image processing
- Figure 8. Local enlargement before and after image processing
- Figure 9. Density-elevation images before and after removal of small area
- Figure 10. Comparison of filter results with optical image
- Figure 11.