| Citation: | PAN Haixia, XU Jialu, LI Jintao, et al. Research and implementation of multi-size aerial image positioning method based on CNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2170-2176. doi: 10.13700/j.bh.1001-5965.2019.0045(in Chinese)
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Image positioning is the key of UAV visual navigation. Scene matching navigation is widely used in traditional UAV visual navigation. With the continuous development of computer technology, deep learning technology provides a new way for the realization of visual navigation. In this context, this research mainly focuses on image localization based on convolution neural network. In this paper, based on the vertical reconnaissance of UAV, the aerial image of flight area is divided into several grids of the same size, each grid represents a class of regions, and the convolutional neural network (CNN) is trained by making data sets of grid images. This paper designs a fully convolutional network model based on AlexNet, which integrates saliency features. It effectively implements a sliding window classifier with CNN multi-size input, and proposes a neighborhood saliency reference positioning strategy to filter the classification results, so as to realize the positioning of multi-size aerial images.
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