A Benchmark Sentinel-1 SAR Dataset for Airport Detection
Daochang Wang, Fan Zhang, Fei Ma, Wei Hu, Yu Ming Tang, Yongsheng Zhou
Abstract
Airports are important targets in both military and civilian tasks. Synthetic aperture radar (SAR) based airport detection has received increasing attention in recent years. However, due to the high cost of the SAR imaging and annotation process, there is no publicly available SAR dataset for airport detection, which leads to the fact that deep learning methods have not been applied to airport detection and no unified evaluation benchmark for traditional methods. To provide a benchmark for airport detection studies in SAR images, a large SAR airport dataset (SAD) is presented in this paper. It contains 624 SAR images from Sentinel 1B, covering 104 instances of airports with different scales, orientations and shapes, which can realistically reflect the real world. Experiments with four deep learning-based methods and three traditional methods on this dataset demonstrate its effectiveness and challenge. It serves for the development of state-of-the-art airport detection algorithms or other related tasks. In addition, we found that airport runways in SAR images always have some parallel line segments. Inspired by this, the fusion of deep features and line segments is achieved by designing a line segment detector branch, which further improves the accuracy of airport detection.