一种基于语义分割引导的地面到航空图像匹配方法
A Semantic Segmentation-guided Approach for Ground-to-Aerial Image Matching
AI总结：
提出语义对齐网络SAN，融合卫星图像及其语义分割掩码，通过三流Siamese网络实现无GPS的地面-卫星图像匹配，在CVUSA数据集上提升各视场性能。
摘要：
如今，地面视角图像的精确地理定位在新闻、法医分析、交通和地球观测等多个领域发挥着重要作用。本研究解决了在没有GPS数据的情况下，将查询地面视角图像与相应卫星图像进行匹配的问题。该方法通过比较地面视角图像和卫星图像的特征来实现匹配，创新性地利用后者的语义分割掩码，通过一个三流Siamese-like网络进行处理。所提出的方法，即语义对齐网络（Semantic Align Net，SAN），专注于有限视场（FoV）和地面全景图像（视场为360°的图像）。其新颖之处在于将卫星图像与其语义分割掩码相结合进行融合，旨在确保模型能够提取有用的特征并聚焦于图像的重要部分。本研究展示了SAN通过对图像的语义分析，在未标注的CVUSA数据集上，对所有测试的视场均提升了性能。
英文摘要：
Nowadays the accurate geo-localization of ground-view images has an important role across domains as diverse as journalism, forensics analysis, transports, and Earth Observation. This work addresses the problem of matching a query ground-view image with the corresponding satellite image without GPS data. This is done by comparing the features from a ground-view image and a satellite one, innovatively leveraging the corresponding latter's segmentation mask through a three-stream Siamese-like network. The proposed method, Semantic Align Net (SAN), focuses on limited Field-of-View (FoV) and ground panorama images (images with a FoV of 360°). The novelty lies in the fusion of satellite images in combination with their semantic segmentation masks, aimed at ensuring that the model can extract useful features and focus on the significant parts of the images. This work shows how SAN through semantic analysis of images improves the performance on the unlabelled CVUSA dataset for all the tested FoVs.
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