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| Citation: | LIU Qi, YU Weidong, and HONG Wen. Vehicle detection in multi-aspect SAR images based on improved GOFRO[J]. Journal of Radars, 2023, 12(5): 1081–1096. doi: 10.12000/JR23042
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Vehicle Detection in Multi-aspect SAR Images Based on Improved GOFRO
DOI: 10.12000/JR23042
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Abstract
Vehicle targets in urban scenes have the characteristics of random distribution and can be easily disturbed by environmental factors during the detection process. Given the above issues, this paper proposes a detection method that utilizes multi-aspect Synthetic Aperture Radar (SAR) images for stationary vehicle target extraction. In the feature extraction stage, a novel feature extraction method called Multiscale Rotational Gabor Odd Filter-based Ratio Operator (MR-GOFRO) is designed for vehicle targets in multi-aspect SAR images, where the original GOFRO features are improved from four aspects—filter form, feature scale, feature direction and feature level. The improvement allows MR-GOFRO to adapt to possible variations in the target direction, scale, morphology, etc. In the image fusion stage, a Weighted-Non-negative Matrix Factorization (W-NMF) method is developed to adjust the feature weights from various images according to the feature quality. This method can reduce the quality degradation of the fusion features due to mutual interference between different aspects. The proposed method is verified on various airborne multi-aspect image datasets. The experimental results revealed that the feature extraction and feature fusion methods proposed in this paper enhance the detection accuracy by an average of 3.69% and 4.67%, respectively, compared with similar methods. -
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References
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- Figure 1. Vehicle targets in multi-aspect SAR images
- Figure 2. Multi-aspect image preprocessing procedure
- Figure 3. Vehicle detection in multi-aspect image
- Figure 4. Comparison of the filtering effects of LoG and Gaussian filter on SAR image
- Figure 5. Schematic diagram of GOFRO and MR-GOFRO
- Figure 6. The imaging geometry of multi-aspect images under near-linear flight model
- Figure 7. Images from the Yangjiang flight experiment
- Figure 8. Locations of the detected scenes in urban area
- Figure 9. Vehicle targets in multi-aspect SAR images in scene 5
- Figure 10. The truth map generating process (Taking scene 2as an example)
- Figure 11. Truth maps of different scenes
- Figure 12. Reference optical images of different scenes
- Figure 13. Detection results in different scenes
- Figure 14. Vehicle detection results in different scenes in Zhoushan flight experiment dataset
- Figure 15. The vehicle detection results in the same scene before and after the addition of MR-GOFRO scaling step
- Figure 16. The vehicle detection results in the same scene before and after the addition of MR-GOFRO direction adjustment step
- Figure 17. The vehicle detection results in the same scene before and after retaining MR-GOFRO texture information
- Figure 18. Detection results obtained by different methods in scene 2
- Figure 19. Detection results under different image quantity conditions