Deep Learning Approaches for Airport Detection in Remote Sensing Images
Summary
Airport detection from remote sensing images has become a critical task for airspace monitoring, infrastructure planning and disaster response. Traditional methods based on hand-crafted features and thresholding have gradually given way to deep learning solutions that deliver end-to-end detection and classification. Convolutional neural networks (CNNs) now form the backbone of most pipelines, enabling hierarchical feature extraction and robust runway localisation across diverse viewing angles and scales. Multi-scale feature fusion, attention mechanisms and transformer architectures have further improved resilience to occlusion, varying illumination and seasonal changes. Thermal infrared and multispectral sensors extend operational capacity to day-night and all-weather conditions, while data augmentation and semi-supervised learning address the scarcity of labelled airport datasets. Cross-modal fusion of optical, LiDAR and radar imagery has demonstrated enhanced detection accuracy under challenging environmental conditions, paving the way for globally scalable monitoring systems.
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Deep Learning Approaches for Airport Detection in Remote Sensing Images publication trend
The graph below shows the total number of articles in deep learning approaches for airport detection in remote sensing images across all publications each year (not limited to Nature Index journals).
Technical terms
Convolutional Neural Network (CNN): A class of deep network that applies convolutional filters to extract hierarchical spatial features from images.
Transfer Learning: A technique where a pre-trained model on a large dataset is fine-tuned on a target task with limited data.
YOLO (You Only Look Once): A family of single-stage object detectors that partition input images into grids for simultaneous prediction of bounding boxes and class probabilities.
Attention Mechanism: A module that reweights feature maps to focus on salient regions, enhancing representation of critical structures.
Spatial Pyramid Pooling: A layer that pools features at multiple scales to enable detection of objects with varying sizes in a single network.
References
- On-orbit monitoring flying aircraft day and night based on SDGSAT-1 thermal infrared dataset. Remote Sensing of Environment (2023).
- End-to-End Airplane Detection Using Transfer Learning in Remote Sensing Images. Remote Sensing (2018).
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