TransRes: A Deep Transfer Learning Approach to Migratable Image Super-Resolution in Remote Urban Sensing

Recent advances in remote sensing provide a powerful and scalable sensing paradigm to capture abundant visual information about the urban environments. We refer to such a sensing paradigm as remote urban sensing. In this paper, we focus on a migratable satellite image super-resolution problem in remote urban sensing applications. Our goal is to reconstruct satellite images of a high resolution in a target area where the high-resolution training data is not available by transferring a super-resolution model learned in a source area where such data is available. This problem is motivated by the limitation of current solutions that primarily rely on a rich set of high-resolution satellite images in the studied area that are not always available. Two important challenges exist in solving our problem: i) the target and source areas often have very different urban characteristics that prevent the direct application of a super-resolution model learned from the source area to the target area; ii) it is not a trivial task to ensure effective model migration with desirable quality without sufficient high quality training data. To address the above challenges, we develop TransRes, a deep adversarial transfer learning framework, to effectively reconstruct high-resolution satellite images without requiring any ground-truth training data from the studied area. We evaluate the TransRes framework using the real-world satellite imagery data collected from three different cities in Europe. The results show that TransRes consistently outperforms the state-of-the-art baselines by achieving the lowest perception errors under various application scenarios.