Multi-modal image fusion of visible and infrared for precise positioning of UAVs in agricultural fields

• First time using visible and infrared fusion images to assist drone positioning; • Preprocess images using a modal-quality aware network; • Low-frequency noise suppressed using interactive enhanced fusion network; • Introduced local signal-to-noise ratio into the image fusion loss function. Image matching is a common method to assist drone positioning in agriculture, but it is affected by environmental changes. We propose a scene matching method based on Multi-modal image fusion to enable precise positioning of unmanned aerial vehicles (UAVs). We develop a fusion network that uses a local attention mechanism for visible and infrared images, which filters out low-frequency vegetation information and improves the matching accuracy using satellite images. Moreover, we incorporate an interaction mechanism that adaptively enhances the low-quality modal. Experimental results show that the proposed method reduces the average positioning error by more than 84 % compared to using a single modality, and achieves an error of less than 2.5 m. The experimental results show that our method can enable UAVs to perform precise positioning in the agricultural environment.