摘要
太阳暗条作为太阳大气磁场的示踪,对研究太阳磁场有极其重要的意义。针对现有的暗条检测方法存在检测精度不高,弱小暗条错检、漏检等问题,提出一种基于改进VNet网络的太阳暗条检测方法。首先,使用大熊湖天文台Hα全日面图像并结合磁图制作了太阳暗条数据集;其次,在VNet网络下采样部分采用Inception模块融合不同尺度特征图的特征,同时加入注意力机制增强特征图中暗条部分的语义信息;最后在上采样部分引入深度监督模块,更多地保留太阳暗条的细节特征。为验证算法性能,采用191幅Hα全日面图像数据集,其中包含暗条共3372条。算法在测试数据集上平均准确率达到0.9883,F1值达到0.8385。实验结果证明,该方法可以有效识别Hα全日面图中的暗条。
The Solar filaments is a common solar activity,and the efficient and accurate detection of the solar filaments is of great significance to the study of the solar magnetic field.Due to the fact that the full-plane image has the characteristics of dimming,uneven brightness,and the inaccuracy of the labeled data set,the result of dark strip segmentation is not accurate,weak and small filaments are missed,and darker background parts are falsely detected as filaments,and with other issues.Therefore,a method for detecting solar filaments based on improved VNet network is proposed.First,the solar dark stripe data set is made by combining the solar magnetic map;secondly,the inception module is used to fuse the features of different scale feature maps in the down-sampling part of the VNet network,and the attention mechanism is added to enhance the semantic information of the dark stripe part of the feature map;Finally,a deep supervision module is introduced in the up-sampling part to retain more detailed features of the sun filaments.In order to verify the performance of the algorithm,we use a data set of 191 full-disk solar images,which contains a total of 3372 filaments.The average accuracy of the algorithm on the test data set is 0.9883,the F1 value is 0.8385.The experimental results prove that this method can effectively identify the filaments in the full-disk solar images.
作者
辛泽寰
尚振宏
Xin Zehuan;Shang Zhenhong(Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China)
出处
《天文研究与技术》
CSCD
2022年第1期54-64,共11页
Astronomical Research & Technology
基金
国家自然科学基金(12063002,11873027)资助.
