Abstract
The automatic extraction of airport runway areas from high-resolution Synthetic Aperture Radar (SAR) images is of great research significance in the military and civilian fields. However, it is still challenging to distinguish the airport from surrounding objects in SAR images. In this article, a new framework is proposed to extract airport runway areas (runways, taxiways, packing lots, and aircrafts) in a fast and automatic manner. The framework is based on the Geospatial Contextual Attention Mechanism (GCAM) for geospatial feature learning and classification, which is employed together with the down-sampling and coordinate mapping modules. To evaluate the performance of the proposed framework, three large-scale Gaofen-3 SAR images with 1m resolution are utilized in the experiment. According to the results, Mean Pixels Accuracy (MPA) and Mean Intersection Over Union (MIOU) of the GCAM are 0.9850 and 0.9536, respectively, which outperform RefineNet, DeepLabV3+, and MDDA. The training time of GCAM for the dataset is 2.25h, and the average testing time for the five SAR images is only 18.15s. Therefore, GCAM can offer rapid and automatic airport detection from high-resolution SAR images with high accuracy, which can further be employed to mark the airport to greatly improve the detection accuracy of the aircrafts.
Highlights
As important transportation hubs and military facilities, the detection of airports from Synthetic Aperture Radar (SAR) images attracts considerable interests for a long time [1]
To validate the proposed framework Geospatial Contextual Attention Mechanism (GCAM) in this article, many large-scale SAR images with 1-m resolution including airports from Gaofen-3 system are utilized in the experiment
The light green box shows the aircraft, which are involved in the parking aprons
Summary
As important transportation hubs and military facilities, the detection of airports from Synthetic Aperture Radar (SAR) images attracts considerable interests for a long time [1]. SAR images are more difficult to interpret than optical images, since the analysis of SAR images is usually more complicated. With the rapid development of SAR imaging techniques, the research in extracting airports from SAR images has gradually increased in recent years, and related analytical approaches have bloomed [3]. The exploration of automatic and fast detection of airport runway areas from high-resolution SAR images has become feasible and attracted considerable research interests. Once runway areas are extracted, the accuracy of aircrafts detection from high-resolution SAR images can be improved greatly
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