Abstract: Due to the limitations of remote sensing imaging technology conditions,it is difficult to obtain sequences of remote sensing images with both high temporal and high spatial resolution simultaneously. However,spatiotemporal fusion techniques can generate remote sensing images with high temporal and high spatial resolution. In recent years,various spatiotemporal fusion methods have emerged,showing good performance but still falling short in effectively extracting meaningful information in feature extraction. A deep learning model based on dual attention mechanism improvement (ADCSTFN) is proposed to address this issue,which enhances the model's ability to preserve global information and reconstruct detailed scenes. In the experiments,Landsat and MODIS data were used as the research subjects,and the proposed method was tested using two open-source datasets and a local dataset,and compared with four commonly used spatiotemporal fusion methods. Experimental results show that the resi-dual network and dual attention mechanism proposed in this paper better extract effective information from images. The use of a deep supervision loss function mitigates the issue of vanishing gradients during backpropagation,optimizes the learning process,and significantly improves the fusion results.

Key words: Spatiotemporal fusion, Remote sensing images, Dual attention mechanism, Landsat, MODIS