Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Computers and Electronics in Agriculture

DOI: 10.1016/j.compag.2025.110024

Authors: Xiaodong Liu, Meibo Lv, Chenyuhao Ma, Zhe Fu, Lei Zhang

Institution: Northwestern Polytechnical University

Language: English

Topic: Advanced Image Fusion Techniques

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,870