Style Alignment based Dynamic Observation Method for UAV-View Geo-localization

• URL: http://arxiv.org/abs/2407.02832v1
• Date: Wed, 3 Jul 2024 06:19:42 GMT
• Title: Style Alignment based Dynamic Observation Method for UAV-View Geo-localization
• Authors: Jie Shao, LingHao Jiang,
• Abstract summary: We propose a style alignment based dynamic observation method for UAV-view geo-localization. Specifically, we introduce a style alignment strategy to transfrom the diverse visual style of drone-view images into a unified satellite images visual style. A dynamic observation module is designed to evaluate the spatial distribution of images by mimicking human observation habits.
• Score: 7.185123213523453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of UAV-view geo-localization is to estimate the localization of a query satellite/drone image by matching it against a reference dataset consisting of drone/satellite images. Though tremendous strides have been made in feature alignment between satellite and drone views, vast differences in both inter and intra-class due to changes in viewpoint, altitude, and lighting remain a huge challenge. In this paper, a style alignment based dynamic observation method for UAV-view geo-localization is proposed to meet the above challenges from two perspectives: visual style transformation and surrounding noise control. Specifically, we introduce a style alignment strategy to transfrom the diverse visual style of drone-view images into a unified satellite images visual style. Then a dynamic observation module is designed to evaluate the spatial distribution of images by mimicking human observation habits. It is featured by the hierarchical attention block (HAB) with a dual-square-ring stream structure, to reduce surrounding noise and geographical deformation. In addition, we propose a deconstruction loss to push away features of different geo-tags and squeeze knowledge from unmatched images by correlation calculation. The experimental results demonstrate the state-of-the-art performance of our model on benchmarked datasets. In particular, when compared to the prior art on University-1652, our results surpass the best of them (FSRA), while only requiring 2x fewer parameters. Code will be released at https://github.com/Xcco1/SA\_DOM

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