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In order to solve the problem of too much blurred noise at the edge of satellite remote sensing image, which leads to low image definition, an edge detection method of satellite remote sensing image based on deep learning is proposed. Using the Softmax classifier structure, the data information parameters at the edge image nodes are extracted, and the deep learning algorithm is followed to complete the convolution and pooling of image information, and realize the recognition of satellite remote sensing images based on deep learning. According to the definition principle of scale space, the location of edge detection feature points is determined, and the result of gradient information entropy calculation is combined to complete the splicing of satellite remote sensing images. Calculate the specific values ??of the first-order differential edge operator and the second-order differential edge operator respectively, determine the value range of the gradient amplitude, summarize the known numerical parameters, establish a complete double-threshold expression, and complete the satellite remote sensing image based on deep learning. Design of edge detection methods. The experimental results show that the signal-to-noise ratio index at the edge nodes of satellite remote sensing images increases after the application of the proposed method, which can effectively control the impact of blurred noise on image clarity, and has strong practicability in accurate edge detection of satellite remote sensing images.