Vehicle Detection in Multi-aspect SAR Images Based on Improved GOFRO

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.

Keywords

• multi-aspect sar
• vehicle target detection
• feature extraction
• feature selection
• image fusion