Temporally-Guided Total Variation For Robust Spatiotemporal Fusion Of Satellite Images

Abstract

This paper proposes a new regularization function specific to Spatiotemporal (ST) fusion, named temporally-guided total variation (TGTV). ST fusion is a promising approach to address a trade-off between the temporal and spatial resolution of satellite images. In general, satellite images are severely degraded by noise due to the observation instrument and environment. However, existing ST fusion methods designed to be robust to noise have some limitations, such as only being robust to local noise and oversmoothing without capturing detailed spatial structure. To address these challenges, TGTV is designed according to a reference image that is temporally different from a target image, but is expected to have a similar spatial structure. Then, we provide a new robust ST fusion framework based on TGTV. Experimental results show that our method performs as well as or better than several state-of-the-art ST fusion methods in noiseless cases and outperforms them in noisy cases.