SAD: A Large-scale Dataset towards Airport Detection in Synthetic Aperture Radar Images

TL;DR

This paper introduces a large-scale SAR airport dataset (SAD) to facilitate deep learning research in airport detection within synthetic aperture radar images, addressing the lack of publicly available data.

Contribution

It provides the first extensive SAR airport dataset with 624 images and 104 airfield instances, enabling benchmarking and development of advanced detection algorithms.

Findings

Deep learning methods perform effectively on SAD.

The dataset supports development of state-of-the-art airport detection algorithms.

SAD fills a critical gap in SAR image analysis resources.

Abstract

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.