Multi-Sensor Fusion for UAV Remote Sensing: Deep Learning-Driven Target Detection in Complex Environments

Dear Colleagues,

Unmanned Aerial Vehicle (UAV) remote sensing has emerged as a transformative technology in wide-spread applications including environmental monitoring, disaster response and security surveillance, due to the unparalleled flexibility, high spatial resolution, cost-efficiency and so on. However, the complex environments characterized by variable illumination, obstacle occlusion and cluttered background pose significant challenges to the target detection task for traditional single-modal UAV remote sensing systems. Specifically, single-modal sensing (e.g., optical, thermal, radar, or LiDAR) often suffers from inherent limitations. For instance, optical sensors are susceptible to illumination changes and occlusion, thermal sensors lack spatial detail, and radar/LiDAR may struggle with fine-grained target discrimination. In this context, multi-sensor fusion (MSF) has become a critical solution to complement the strengths and mitigate the weaknesses of individual sensors, enabling more robust and reliable data acquisition. Meanwhile, the rapid advancement of deep learning (DL) techniques such as convolutional neural networks, transformer models, and point cloud processing frameworks has revolutionized feature extraction and target recognition, providing powerful tools to model complex relationships in fused multi-modal data. The synergy between MSF and DL has thus become a pivotal research direction, addressing the urgent need for accurate, real-time target detection in complex UAV remote sensing scenarios and driving innovations in both academic research and practical applications.

This Special Issue aims to showcase cutting-edge research on multi-sensor fusion for UAV remote sensing, with a specific focus on deep learning-driven target detection in complex environments. This Special Issue aligns closely with the scope of journals focusing on remote sensing, sensor technology, artificial intelligence, and aerospace engineering, as it bridges the gap between multi-sensor system design, deep learning algorithm development, and real-world UAV applications. By integrating insights from multiple disciplines, this Special Issue will provide a comprehensive platform for researchers and practitioners to share innovations, discuss critical issues, and advance the state-of-the-art in intelligent target detection for UAV remote sensing in complex environments.

This Special Issue seeks to bring together original research articles, reviews, and technical notes that explore novel methodologies, validate practical applications, and address key challenges in this interdisciplinary field. Topics of interest include, but are not limited to, multi-modal data fusion strategies, DL-based feature learning for heterogeneous sensor data, target detection models optimized for UAV platforms, non-line-of-sight (NLOS) target detection and application-specific solutions (e.g., disaster monitoring and post-disaster assessment, crop monitoring, ecological environment monitoring, military surveillance or infrastructure inspection).

Prof. Dr. Yong Jia
Prof. Dr. Shisheng Guo
Dr. Yongping Song
Guest Editors

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• UAV remote sensing
• multi-sensor fusion
• deep learning
• complex environments
• target detection
• multimodal data registration