Feature Relation Guided Cross-View Image Based Geo-Localization

作者：Qingfeng Hou, Jun Lu, Haitao Guo, Xiangyun Liu, Zhihui Gong, Kun Zhu, Yifan Ping · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15205029 · 被引用次数：5 · 研究领域：Advanced Image and Video Retrieval Techniques、Multimodal Machine Learning Applications、Robotics and Sensor-Based Localization

The goal of cross-view image based geo-localization is to determine the location of a given street-view image by matching it with a collection of geo-tagged aerial images, which has important applications in the fields of remote sensing information utilization and augmented reality. Most current cross-view image based geo-localization methods focus on the image content and ignore the relations between feature nodes, resulting in insufficient mining of effective information. To address this problem, this study proposes feature relation guided cross-view image based geo-localization. This method first processes aerial remote sensing images using a polar transform to achieve the geometric coarse alignment of ground-to-aerial images, and then realizes local contextual feature concern and global feature correlation modeling of the images through the feature relation guided attention generation module designed in this study. Specifically, the module includes two branches of deformable convolution based multiscale contextual feature extraction and global spatial relations mining, which effectively capture global structural information between feature nodes at different locations while correlating contextual features and guiding global feature attention generation. Finally, a novel feature aggregation module, MixVPR, is introduced to aggregate global feature descriptors to accomplish image matching and localization. After experimental validation, the cross-view image based geo-locali...