AIR-SARSHIP-1.0: High-resolution SAR Ship Detection Dataset

被引：6

作者：

Sun X. ^{[1
,2
,3
]}

Wang Z. ^{[1
,3
]}

Sun Y. ^{[1
,2
]}

Diao W. ^{[1
,3
]}

Zhang Y. ^{[1
,3
]}

Fu K. ^{[1
,2
,3
]}

机构：

[1] Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing

[2] University of Chinese Academy of Sciences, Beijing

[3] Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing

来源：

Journal of Radars | 2019年 / 8卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Deep learning; Public dataset; SAR ship detection;

D O I：

10.12000/JR19097

中图分类号：

学科分类号：

摘要：

Over the recent years, deep-learning technology has been widely used. However, in research based on Synthetic Aperture Radar (SAR) ship target detection, it is difficult to support the training of a deep-learning network model because of the difficulty in data acquisition and the small scale of the samples. This paper provides a SAR ship detection dataset with a high resolution and large-scale images. This dataset comprises 31 images from Gaofen-3 satellite SAR images, including harbors, islands, reefs, and the sea surface in different conditions. The backgrounds include various scenarios such as the near shore and open sea. We conducted experiments using both traditional detection algorithms and deep-learning algorithms and observed the densely connected end-to-end neural network to achieve the highest average precision of 88.1%. Based on the experiments and performance analysis, corresponding benchmarks are provided as a basis for further research on SAR ship detection using this dataset. © 2019 Institute of Electronics Chinese Academy of Sciences. All rights reserved.

引用

页码：852 / 862

页数：10

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