Abstract: With the explosive growth of smart terminal equipment and the rapid rise of mobile Internet,in many scenarios,such as indoor environments and remote mountainous areas with sparse population,the demand for location-based services has become more and more prominent.However,because GPS signals in these areas are blocked or the signal base stations are difficult tocover,GPS location can not working properly.Image based geo-location refersto determine the location of an image based only on visual information.Without any prior knowledge,predicting the geographic location of a photo is a very difficult task,because the images taken from the earth will show huge changes with different weather,objects or camera settings.This paper attempts to explore the cross-view geo-localization method.First,the inverse polar coordinate transformation is used to convert the street view perspective to the spatial perspective image,so as to reduce the domain gap between the two.Then deep learning is used to encode images from different perspectives to obtain more robust global vector descriptors.Finally,performing image matching on this basis.In the aspect of image feature extraction,the VGG16 model is adopted,and a smaller convolution kernel with deeper layers is used to increase the perception field of the network model and save parameters.In terms of feature encoding,the multi-scale attention mechanism is integrated into the NetVLAD model,and the features extracted from the backbone model are encoded into a more robust global feature descriptor vector.Experimental results show that the above-mentioned method can achieve higher accuracy,compared with the existing methods.And without the high-definition street view captured by professional equipment,the street view captured by ordinary smart phones can obtain good matching accuracy.

Key words: Cross-view geo-localization, Inverse polar transform, NetVLAD, Multi-scale attention