A geo-localization method is proposed for military and civilian applications,which is used when no global navigation satellite system(GNSS)information is available.The open graphics library(OpenGL)is used to build a three-dimensional geographic model of the test area using digital elevation model(DEM)data,and the skyline can thus be extracted with the model to form a database.Then,MultiSkip DeepLab(MS-DeepLab),a fully convolutional semantic segmentation network with multiple skip structures,is proposed to extract the skyline from the query image.Finally,a matching model based on convolutional neural network(CNN)feature is adopted to calculate the similarity between the skyline features of the query image and the DEM database to realize automatic geo-localization.The ex-periments are conducted at a 202.6 km2 test site in north-eastern Changsha,China.50 test points are selected to verify the effectiveness of the proposed method,and an excellent result with an average positioning error of 49.29 m is ob-tained.
TANG Jin、GONG Cheng、GUO Fan、YANG Zirong、WU Zhihu
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School of Computer Science and Engineering,Central South University,Changsha 410083,China
School of Automation,Central South University,Changsha 410083,China
国家自然科学基金Science&Technology Innovation System for National Defense of ChinaScience&Technology Innovation System for National Defense of China
NETL
NSTL
万方数据
维普
