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In the past several “five-year plans”, China has basically established the national, provincial and regional streets of basic geographic information database, and on this basis has carried on a certain degree of improvement. Based on deep learning, this paper aims at improving the boundary accuracy of the orthographic composite image based on the convolutional neural network algorithm. It mainly compares the effects of different parameters on the performance of the network model, and describes the training and testing performance of the model through the corresponding performance evaluation method. In the research and test, the detection accuracy statistics of internal industry encryption points and field control points with different scale accuracy were adopted. The results showed that the error in the DOM detection of new aerial images and satellite images was less than 3m and 9m, which met the technical requirements and improved the boundary accuracy compared with the plane accuracy of existing digital orthophoto images. Thus, it can be seen that the new production technical scheme of aerial image/satellite shadow DOM generated by enhancing the precision of the orthographic composite image boundary based on the full convolutional network algorithm under deep learning is efficient and feasible. The study not only improves the relevant theories, but also has some practical inspirations for future research.

Full Convolutional Network Algorithm Improves the Accuracy of the Boundary of Orthographic Composite Images