A Deep Learning-based Super-resolution Model for Bistatic SAR Image

Abstract

Target detection and recognition of bistatic SAR image has been widely studied in recently. However, how to accurately detect and recognize targets with low-resolution and small size in the image is still a problem. Image super-resolution reconstruction technology can increase image resolution and expand target size to improve detection and recognition performance. Thus, we in this paper bring deep learning onto the topic of bistatic SAR image super-resolution reconstruction and propose a novel super-resolution reconstruction model (named FSRCNN) for bistatic SAR images. The proposed model is characterized by a feature extractor with different structures, a feedback feature enhancement block, and a feature fusion module. During the experiments of bistatic SAR measured data, our method is proved to perform clearer visual effects than other image super-resolution reconstruction method. Moreover, our model achieves the best value on peak signal to noise ratio and structural similarity, which indicates that our model can effectively super-resolution reconstruct bistatic SAR images.