SAD: A Large-scale Dataset towards Airport Detection in Synthetic Aperture Radar Images
This provides a benchmark for airport detection in SAR images, benefiting researchers in remote sensing and military/civilian domains, though it is incremental as it focuses on dataset creation rather than novel algorithmic advances.
The paper introduces SAD, a large-scale synthetic aperture radar (SAR) dataset containing 624 images and 104 airfield instances to address the lack of public data for airport detection, enabling the application of deep learning methods with proven effectiveness in experiments.
Airports have an important role in both military and civilian domains. The synthetic aperture radar (SAR) based airport detection has received increasing attention in recent years. However, due to the high cost of SAR imaging and annotation process, there is no publicly available SAR dataset for airport detection. As a result, deep learning methods have not been fully used in airport detection tasks. To provide a benchmark for airport detection research in SAR images, this paper introduces a large-scale SAR Airport Dataset (SAD). In order to adequately reflect the demands of real world applications, it contains 624 SAR images from Sentinel 1B and covers 104 airfield instances with different scales, orientations and shapes. The experiments of multiple deep learning approach on this dataset proves its effectiveness. It developing state-of-the-art airport area detection algorithms or other relevant tasks.