A New Spatio-Temporal Fusion Method for Remotely Sensed Data Based on Convolutional Neural Networks

In some remote sensing applications such as change detection, satellite images with both high spatial and high temporal resolution are required. However, no single satellite sensor can currently provide such images due to technical specifications. To solve this problem, spatio-temporal fusion provides a cost-effective solution. In this paper, we propose a new spatio-temporal fusion approach, based on convolutional neural networks (CNNs), for Landsat and MODIS image fusion. Specifically, the proposed approach utilizes CNNs to model the heterogeneity of fine pixels from the coarse MODIS images. Here, the heterogeneity of fine pixels is defined as the difference between the reflectance changes obtained from the two types of images. After that, two transition-predicted images can be obtained using the trained CNNs, which are then fused in order to obtain a fi-nal prediction. In our newly proposed approach, CNNs are only used to learn the heterogeneity of fine pixels rather than the whole images, thus providing a more stable and less time-consuming strategy as compared to other available approaches. We evaluated the proposed approach on a public spatio-temporal fusion dataset and the obtained results suggest that our newly developed method achieves state-of-the-art performance.