Effects Analysis of SAR Data Quantization on Deep Learning-Based Target Detection Task

Since the dynamic range of synthetic aperture radar (SAR) data is extremely large, SAR data quantization is required for storage and display of SAR images. The quantization process may cause undesirable change in the characteristics of the targets on the images, making it challenging to effectively detect targets in deep learning-based target detection task. To address this problem, a multi-quantization-based detection method is proposed in this paper. First, the effect of different quantization method is analysed and a multi-quantization based data augmentation strategy is proposed. Second, the labeling of SAR targets is analysed in response to the inconsistency between target scattering characteristics and physical contour shapes and the issue of partial visiblity. Then, the multi-quantization-based detection method is proposed to obtain more stable and complete detection results. The experiments conducted on the AIR-SARShip dataset demonstrate the effectiveness of the proposed method.