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Image retrieval (IR) has emerged as a promising approach forself-localization in unmanned aerial vehicles (UAVs). However, IR-based methodsface several challenges: 1) Pre- and post-processing incur significantcomputational and storage overhead; 2) The lack of interaction betweendual-source features impairs precise spatial perception. In this paper, wepropose an efficient heterogeneous spatial feature interaction method, termedDrone Referring Localization (DRL), which aims to localize UAV-view imageswithin satellite imagery. Unlike conventional methods that treat different datasources in isolation, followed by cosine similarity computations, DRLfacilitates the learnable interaction of heterogeneous features. To implementthe proposed DRL, we design two transformer-based frameworks, Post-Fusion andMix-Fusion, enabling end-to-end training and inference. Furthermore, weintroduce random scale cropping and weight balance loss techniques to augmentpaired data and optimize the balance between positive and negative sampleweights. Additionally, we construct a new dataset, UL14, and establish abenchmark tailored to the DRL framework. Compared to traditional IR methods,DRL achieves superior localization accuracy (MA@20 +9.4\%) while significantlyreducing computational time (1/7) and storage overhead (1/3). The dataset andcode will be made publicly available. The dataset and code are available at\url{https://github.com/Dmmm1997/DRL} .

Drone Referring Localization: An Efficient Heterogeneous Spatial Feature Interaction Method For UAV Self-Localization