Satellite Image Small Target Application Based on Deep Segmented Residual Neural Network

This study employs a deep segmented residual neural network model to analyze the super-resolution of a single satellite image. A deep convolutional neural network model was analyzed, and its performance was improved. We proposed two residual layers to divide the deep network into two groups, the sum of the two residuals is the total residual, which can minimize the residual loss function and enhance the network performance. The experimental model achieved high peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) than networks without the proposed improvements when tested on satellite images. Considering these results, the application of this technology will be significant for further research on satellite images.