  Image retrieval has emerged as a prominent solution for the self-localization
task of unmanned aerial vehicles (UAVs). However, this approach involves
complicated pre-processing and post-processing operations, placing significant
demands on both computational and storage resources. To mitigate this issue,
this paper presents an end-to-end positioning framework, namely Finding Point
with Image (FPI), which aims to directly identify the corresponding location of
a UAV in satellite-view images via a UAV-view image. To validate the
practicality of our framework, we construct a paired dataset, namely UL14, that
consists of UAV and satellite views. In addition, we establish two
transformer-based baseline models, Post Fusion and Mix Fusion, for end-to-end
training and inference. Through experiments, we can conclude that fusion in the
backbone network can achieve better performance than later fusion. Furthermore,
considering the singleness of paired images, Random Scale Crop (RSC) is
proposed to enrich the diversity of the paired data. Also, the ratio and weight
of positive and negative samples play a key role in model convergence.
Therefore, we conducted experimental verification and proposed a Weight Balance
Loss (WBL) to weigh the impact of positive and negative samples. Last, our
proposed baseline based on Mix Fusion structure exhibits superior performance
in time and storage efficiency, amounting to just 1/24 and 1/68, respectively,
while delivering comparable or even superior performance compared to the image
retrieval method. The dataset and code will be made publicly available.
