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In order to improve the experimental effect of SRCNN under spatio-temporal fusion, the paper, taking Landsat8 OLI and MODIS images as examples, adopts the detailed information of adjacent time-phase high-resolution images as a prior information into the network input on the basis of SRCNN and introduces the attention mechanism. It proposes an improved SRCNN remote sensing image spatio-temporal fusion on the basis of multi-stream data input and attention mechanism (MBA-SRCNN). The improved SRCNN remote sensing image spatio-temporal fusion based on multi-stream data input (M-SRCNN) is taken as a comparative experiment. The results show that M-SRCNN had significantly improved the experimental effect when compared with SRCNN. In the evaluation of the entire image, PSNR reached 32.9252 and SSIM reached 0.8712, which were optimized by 4.0667 and 0.0840 respectively, and the optimization range of RED band was the largest one. M-SRCNN improved the distortion and edge blur of SRCNN, and the specific performance was significantly enhanced. On the basis of M-SRCNN, MBA-SRCNN had optimized PSNR by 1.3847 and SSIM by 0.0247, which enhances the reconstruction effect of low-frequency information. The MBA-SRCNN constructed in this study can generate remote sensing images with high temporal and spatial resolution more accurately, which has a certain significance for the research in the field of spatio-temporal fusion of remote sensing images.
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