Ground-Guided Conditional Pixel Synthesizer for Height-based Satellite Imagery Super-Resolution
Yang Xu, Kejie Huang, Chenggang Clarence Yan · 2024
Remote sensing plays a crucial role in various fields. However, challenges associated with acquiring high-resolution data from satellite cameras significantly limit their practical applications. The high semantic density per pixel in satellite images makes it challenging for existing methods to extract adequate geometric and semantic information from extremely low-resolution inputs for super-resolution reconstruction. This paper introduces a satellite imagery super-resolution architecture guided by ground-view images, framing the problem as neural pixel synthesis with satellite camera height as a variable factor. This approach proposes a hypergraph-based cross-view mapper module that achieves low-order geometric registration and high-order feature fusion by capturing cross-view visual correlations, accompanied by a height-based pixel synthesizer for continuous multi-level super-resolution, conceptualized as neural rendering. Furthermore, we have developed a multi-level resolution satellite image dataset, complete with ground images from corresponding locations. Extensive experiments on diverse datasets validate the effectiveness of our proposed method in a range of application scenarios.