摘要
针对进行实际大场景合成孔径雷达(SAR)图像舰船检测时容易出现众多陆地上的虚警问题,文中提出一种基于纯背景混合训练的方法来抑制大场景SAR舰船检测的虚警。该方法的核心是将不含有舰船的图像样本(纯背景样本)也输入到网络中进行训练,使网络能够学习纯背景样本特征,最终实现陆地上一些与舰船相似度高的强散射亮点的虚警抑制。由于现有公开的数据集缺少纯背景图像样本,为了便于验证该方法的有效性,文中还组建了由10幅Sentinel-1大场景SAR图像组成的纯背景混合训练SAR舰船检测数据集。在该数据集上,两种单阶段检测器(RetinaNet和SSD)和两种双阶段检测器(Faster R-CNN和Cascade R-CNN)的实验对比结果表明纯背景混合训练可以有效抑制大场景SAR图像中舰船检测的虚警。
In order to solve the problem that many land false alsrms accur in large-scene SAR images ship detection,a method is proposed based on pure background hybrid training to suppress false alarms of large-scene SAR ship detection.The core of this method is to input the image samples without ships(pure background samples)into networks for training,so as to learn the features of pure background samples,and finally realize the false alarm suppression of some strong scattering bright spots on land with high similarity to ships.Due to the lack of pure background image samples in existing public dataset,to verify the effectiveness of this method,a pure background hybrid training SAR ship detection dataset that consists of 10 Sentinel-1 large-scene SAR images is also constructed.The experimental results of two kinds of one-stage detectors(Retinanet and SSD)and two kinds of two-stage detectors(Fast R-CNN and Cascade R-CNN)on this dataset show that pure background hybrid training can effectively suppress the false alarms of ship detection in large-scene SAR images.
作者
张天文
张晓玲
ZHANG Tianwen;ZHANG Xiaoling(School of Information and Communication Technology,University of Electronic Science and Technology of China)
出处
《现代雷达》
CSCD
北大核心
2022年第2期1-8,共8页
Modern Radar
基金
国家自然科学基金资助项目(61571099)。
关键词
合成孔径雷达舰船检测
虚警抑制
大场景
深度学习
纯背景混合训练
synthetic aperture radar ship detection
false alarm suppression
large-scene
deep learning
pure background hybrid training