A REMOTE SENSING SPATIOTEMPORAL FUSION MODEL OF LANDSAT AND MODIS DATA VIA DEEP LEARNING

In this paper, a novel spatiotemporal fusion model based on deep learning is proposed, which handles the huge spatial resolution gap and the nonlinear mapping between the high spatial resolution (HSR) image and the corresponding high temporal resolution (HTR) image at the same imaging time. Considering the huge spatial resolution gap, a two-layer fusion strategy is adopted. In each layer, the convolutional neural network (CNN) model is employed to exploit the non-linear mapping between the HSR and HTR image and reconstruct the high-spatial and high-temporal (HSHT) resolution images. In the experiment, Landsat data is the representation of the high spatial resolution images, MODIS data is used as the corresponding low spatial resolution images. The experimental results on two different datasets clearly illustrate the superiority of the proposed model.