A Spatiotemporal Fusion Based Cloud Removal Method for Remote Sensing Images With Land Cover Changes

Cloud contamination greatly limits the potential utilization of optical remote sensing images for geoscience applications. Many solutions have been developed to remove the clouds from multispectral images. Among these approaches, the temporal-based methods which borrow complementary information from multitemporal images outperform the other methods. However, the common fundamental supposition of the temporal-based methods decides that they are only suitable for scenes with phenological changes, while they perform poorly in cases with significant land cover changes. In this paper, a cloud removal procedure based on multisource data fusion is developed to overcome this limitation. On the basis of the temporal-based approaches, which employ a cloud-free image as reference, this method further introduces two auxiliary images with similar wavelengths and close acquisition dates to the reference and target (contaminated) images into the reconstruction process. The temporal variability of the land cover is captured from the two auxiliary images through a modified spatiotemporal data fusion model, and thus, the serious errors produced by the temporal-based methods can be avoided. Moreover, a residual correction strategy based on the Poisson equation is used to enhance the spectral coherence between the recovered and remaining regions. The experiments confirmed that the proposed method can perform very well for cases with significant land cover changes. Compared with some state-of-the-art approaches, it produces lower bias and more robust efficacy. In conclusion, our method will act as an important technical supplement to the current cloud removal framework, and it provides the possibility to handle scenes with significant land cover changes.