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

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 air...

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Bibliographic Details
Published inarXiv.org
Main Authors Wang, Daochang, Zhang, Fan, Ma, Fei, Hu, Wei, Tang, Yu, Zhou, Yongsheng
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 07.04.2022
Subjects
Airports
Algorithms
Annotations
Datasets
Deep learning
Machine learning
Radar detection
Radar imaging
Synthetic aperture radar
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Summary: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.
ISSN:2331-8422