Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer
This addresses the challenge of obtaining human annotations for remote sensing imagery, which is incremental as it applies existing adversarial learning techniques to a specific domain.
The paper tackles the problem of urban material recognition in low-resolution satellite imagery by proposing an unsupervised domain adaptation approach using adversarial learning to super-resolve low-resolution images, enabling label transfer from high-resolution annotated data.
Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images. To this end, we propose an unsupervised domain adaptation based approach using adversarial learning. We aim to harvest information from smaller quantities of high resolution data (source domain) and utilize the same to super-resolve low resolution imagery (target domain). This can potentially aid in semantic as well as material label transfer from a richly annotated source to a target domain.