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A Extremely Fast Spatio-Temporal Fusion Method for Remotely Sensed Images

Spatio-temporal fusion has been developed to generate the synthetic remote sensing data with high spatial resolution and high temporal resolution simultaneously. To date, a number of spatio-temporal fusion methods have been developed and most of them are remarkable. However, most the methods are designed to achieve better fusion performance and higher fusion accuracy, while the fusion speed is always ignored. As a matter of fact, most current spatio-temporal fusion methods are time-consuming. To address this defect of spatiotemporal fusion, in this paper we propose a extremely fast spatiotemporal fusion method. The core idea of this method is extracting the spatial information from the prior high-spatial-resolution images and embedding that into the low-spatial-resolution images by local normalization to predict the missing high-spatial-resolution images. In the experiments, two dataset, including a Landsat-MODIS dataset and a Sentienl-MODIS dataset, are adopted to testing this method. The experimental results demonstrate this method can achieve great performance with extremely fast speed.