What is it about?
Cross-view geo-localization in GNSS-denied environments aims to determine an unknown location by matching drone-view images with the correct geo-tagged satellite-view images from a large gallery. Recent research shows that learning discriminative image representations under specific weather conditions can significantly enhance performance. However, the frequent occurrence of unseen extreme weather conditions hinders progress. This paper introduces MCGF, a Multi-weather Cross-view Geo-localization Framework designed to adapt to unseen weather conditions dynamically. MCGF establishes a joint optimization between image restoration and geo-localization using denoising diffusion models.
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Photo by Mitch Nielsen on Unsplash
Why is it important?
Varying weather conditions, including fog, rain, snow, wind, light, dark, and combinations of multiple weather types, reduce visibility, corrupt the information captured by an image, significantly complicate image geographic representation and lead to a sharp decline in cross-view geo-localization performance. The greatest contribution of this paper lies in adaptively achieving unbiased image geographic representation under diverse weather conditions.
Perspectives
The method in this paper can be applied to fields such as drone field rescue and natural disaster surveys. Its wide application makes me very happy.
Tongtong Feng
Tsinghua University
Read the Original
This page is a summary of: Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models, October 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3689095.3689103.
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