Complex-Valued SAR Image Super-Resolution via Subaperture Learning and Fusion

Synthetic aperture radar (SAR) imaging is a popularly used tool in many fields, while the contradiction between resolution and coverage is an open problem. The range resolution is relied on the bandwidth of transmitted signals, while the azimuth resolution is dependent on the length of synthesized aperture. They are related to radar configurations, and infeasible to be tuned freely. To solve the problem, a new SAR image super-resolution method is proposed in this paper. It is composed of pre-imaging, feature learning, and reconstruction. In the first phase, the zero-padded technique is used to achieve spatial alignment. The result is then decomposed along the sub-apertures, forming the sub-aperture images and the sub-band images. They were delivered to the second phase for cross-domain learning and hierarchical fusion. The pre-learning, encoder-decoder, and refinement are configured. The learned features are finally tuned for super-resolution reconstruction. On this basis, a new evaluation system was built to measure the performance. Both the supervised evaluation and the unsupervised evaluation are considered. The improvement of 3.78% is achieved for the amplitude components, while the phase accuracy is 4.62% better than the competitor.