Due to technical and budget limitations, there are inevitably some trade-offs in the design of remote sensing instruments, making it difficult to acquire high spatiotemporal resolution remote sensing images simultaneously. To address this problem, this paper proposes a new data fusion model named the deep convolutional spatiotemporal fusion network (DCSTFN), which makes full use of a convolutional neural network (CNN) to derive high spatiotemporal resolution images from remotely sensed images with high temporal but low spatial resolution (HTLS) and low temporal but high spatial resolution (LTHS). The DCSTFN model is composed of three major parts: the expansion of the HTLS images, the extraction of high frequency components from LTHS images, and the fusion of extracted features. The inputs of the proposed network include a pair of HTLS and LTHS reference images from a single day and another HTLS image on the prediction date. Convolution is used to extract key features from inputs, and deconvolution is employed to expand the size of HTLS images. The features extracted from HTLS and LTHS images are then fused with the aid of an equation that accounts for temporal ground coverage changes. The output image on the prediction day has the spatial resolution of LTHS and temporal resolution of HTLS. Overall, the DCSTFN model establishes a complex but direct non-linear mapping between the inputs and the output. Experiments with MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Operational Land Imager (OLI) images show that the proposed CNN-based approach not only achieves state-of-the-art accuracy, but is also more robust than conventional spatiotemporal fusion algorithms. In addition, DCSTFN is a faster and less time-consuming method to perform the data fusion with the trained network, and can potentially be applied to the bulk processing of archived data.
Earth observation data with high spatiotemporal resolution are critical for dynamic monitoring and prediction in geoscience applications, however, due to some technique and budget limitations, it is not easy to acquire satellite images with both high spatial and high temporal resolutions. Spatiotemporal image fusion techniques provide a feasible and economical solution for generating dense-time data with high spatial resolution, pushing the limits of current satellite observation systems. Among existing various fusion algorithms, deeplearningbased models reveal a promising prospect with higher accuracy and robustness. This paper refined and improved the existing deep convolutional spatiotemporal fusion network (DCSTFN) to further boost model prediction accuracy and enhance image quality. The contributions of this paper are twofold. First, the fusion result is improved considerably with brand-new network architecture and a novel compound loss function. Experiments conducted in two different areas demonstrate these improvements by comparing them with existing algorithms. The enhanced DCSTFN model shows superior performance with higher accuracy, vision quality, and robustness. Second, the advantages and disadvantages of existing deeplearningbased spatiotemporal fusion models are comparatively discussed and a network design guide for spatiotemporal fusion is provided as a reference for future research. Those comparisons and guidelines are summarized based on numbers of actual experiments and have promising potentials to be applied for other image sources with customized spatiotemporal fusion networks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.