Metric or Task: A New Perspective of SAR Super-Resolution Imaging

Synthetic aperture radar (SAR) played an important role in target monitoring and identifying. Yet, the imaging resolution and the spatial coverage were contradictory. A great many works were presented previously; yet, little studies were devoted to complex-valued SAR images. In addition, the evaluation strategy customized to the SAR image was unavailable. To address the problems, a new SAR image super-resolution method was proposed in this article. Different from the preceding works, SAR super-resolution was achieved from the perspectives of metric-driven and task-driven, forming the base model (SARSR) and the advanced model [detection task-driven super-resolution (DetSR)]. The base model was composed of three phases: preimaging, feature refinement, and reconstruction. The periodogram spectral estimation technique was first used to achieve spatial alignment. The spectrogram was fed into a hybrid architecture to learn the high-level representations. The learned features were refined and reconstruction. On the basis, the prior knowledge transferred from the target detection task was then used to promote the super-resolution performance, forming the advanced model. Finally, the evaluation system customized to the SAR image was presented. A set of metrics composed of vision and imaging measurements were defined to assess the image quality initially. The task-driven evaluation was then presented. Multiple rounds of experiments were performed to verify the proposed method. The results proved that the metrics can be improved more than 10%, while the detection accuracy can be improved by 4%.