Airports have a profound impact on our lives, and uncovering their distribution around the world has great significance for research and development. However, existing airport databases are incomplete and have a high cost of updating. Thus, a fast and automatic worldwide airport detection method can be of significance for global airport detection at regular intervals. However, previous airport detection studies are usually based on single remote sensing (RS) imagery, which seems an overwhelming burden for worldwide airport detection with traversal searching. Thus, we propose a hierarchical airport detection method consisting of broad-scale extraction of worldwide candidate airport regions based on spatial analysis of released RS products, including impervious surfaces from FROM-GLC10 (fine resolution observation and monitoring of global land cover 10) product, building distribution from OSMs (open street maps) and digital surface model from AW3D30 (ALOS World 3D—30 m). Moreover, narrow-scale aircraft detection was initially conducted by the Faster R-CNN (regional-convolutional neural networks) deep learning method. To avoid overestimation of background regions by Faster R-CNN, a second CNN classifier is used to refine the class labeling with negative samples. Specifically, our research focuses on target airports with at least 2 km length in three experimental regions. Results show that spatial analysis reduced the possible regions to 0.56% of the total area of 75,691 km2. The initial aircraft detection by Faster R-CNN had a mean user’s accuracy of 88.90% and ensured that all the aircrafts could be detected. Then, by introducing the CNN reclassifier, the user’s accuracy of aircraft detection was significantly increased to 94.21%. Finally, through an experienced threshold of aircraft number, 19 of the total 20 airports were detected correctly. Our results reveal the overall workflow is reliable for automatic and rapid airport detection around the world with the help of released RS products. This research promotes the application and progression of deep learning.
A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning
