To meet the demand of high spatio-temporal resolution remote sensing images for power facilities safety monitoring and emergency management， a deep convolutional network-based remote sensing image fusion enhancement model for power facilities was proposed. Firstly， a deep convolutional network was designed， including encoder， Residual Attention （RA） mechanism block， substitution attention mechanism block and decoder. Secondly， the two-layer convolution and the residual block of fusion channel attention mechanism were improved to increase the network's attention to details and key features of images， and enhance the feature extraction capability of the network. Thirdly， the multi-channel substitution attention block was improved to make the network paying more attention to the details of images. As the result， the performance of high-resolution image fusion reconstruction was improved. Finally， the loss function composition of the model was improved， and the composite loss function consisting of content loss and visual loss was adopted to improve training effect of the model. Experimental results indicate that the proposed model has the performance of image fusion reconstruction better than other fusion models significantly， and the detail textures of predicted image closer to those of the real image. Compared with Multi-stage Feature Compensation NET （MFCNET） model， the proposed model has the Correlation Coefficient （CC） improved by 1.6%. and the SSIM （Structure Similarity Index Measure） improved by 18.4%. It can be seen that the proposed model provides a basis for remote sensing image processing， especially for high-resolution reconstruction of small target remote sensing images.