At present, deep convolutional neural network (CNN) has been successfully applied to synthetic aperture radar (SAR) target recognition, and achieved good recognition effect. Compared with traditional methods, the recognition performance has been significantly improved. However, in practical applications, the resources of data processing platform are very limited, the computation and the memory cost of deep convolutional neural network are high. These two factors hinder its smooth deployment on embedded devices. This paper proposed a lightweight neural network design strategy combined with knowledge distillation for target recognition. First, a convolutional network model is designed based on the improved inverted residual structure, and a lightweight neural network is obtained, which is used as a student network. Then, the teacher network (a well-trained deep network model) is used to perform knowledge distillation, which affects the student network. training to improve the recognition accuracy. Finally, the trained student network is used to complete the 10-category target recognition in the MSTAR dataset.
As Video synthetic aperture radar (SAR) technology has been developing rapidly in recent years, moving target detection and tracking has gradually become a research hotspot in the field of SAR. Since moving targets in Video SAR produce relatively clear shadows at their real locations, the shadow-based approach provides a new method for ground moving target detection. In this paper, a new approach based on image fusion enhancement is proposed to improve the extraction effect of target shadow in single frame Video SAR image, and the process of shadow segmentation is studied accordingly. First, we use Median Filter to denoise the image, and then use a variety of image enhancement methods to improve the contrast between shadows and background, including piecewise linear stretching, histogram specification, and S-curve enhancement, then use adaptive threshold segmentation algorithm to realize the separation of background and target shadow, finally use morphological processing method to further highlight the target shadow. The effectiveness of the proposed approach is verified on the Video SAR dataset published by Sandia Lab.
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