Auroral Oval Morphology Extraction Based on U-net from Ultraviolet Aurora Observation
-
摘要: 极光卵形态提取是极光研究的重要手段.如何提高强干扰背景下的紫外极光图像极光卵形态提取精度,目前仍是一个难题.本文提出一种基于深度学习语义分割模型U-net的方法,实现了对极光卵形态的高精度提取.在Polar卫星紫外极光观测数据的实验结果表明,该方法相比于已有算法精度更高,对完整型极光卵和缺口型极光卵图像均能得到更加精确的提取结果,特别是针对强日辉干扰、灰度不均匀和对比度低情况下的紫外极光图像时,该方法显示了明显优势.Abstract: Auroral oval morphology extraction plays an important role in the aurora research. How to improve the accuracy of auroral oval morphology extraction in ultraviolet aurora images with strong interference background is still an incomplete problem. In this paper, a method based on deep learning semantic segmentation model U-net is proposed. U-net model with residual block is used to extract auroral oval morphology with high accuracy. The experimental results on Polar satellite ultraviolet aurora images show that this method can get higher accuracy compared with the existing algorithms, and can obtain more detailed extraction results for both full auroral oval and gap auroral oval images. This method shows its advantages especially for aurora images with strong dayglow interference, uneven grayscale and low contrast. At the same time, the applicability and effectiveness of supervised deep learning method on auroral oval morphology extraction have been proved.
-
[1] AKASOFU S I. The latitudinal shift of the auroral belt[J]. J. Atmos. Terr. Phys., 1964, 26(12):1167-1174 [2] GERMANY G A, PARKS G K, RANGANATH H, et al. Analysis of auroral morphology:substorm precursor and onset on January 10, 1997[J]. Geophys. Res. Lett., 1998, 25(15):3043-3046 [3] MILAN S E, LESTER M, COWLEY S W H, et al. Variations in the polar cap area during two substorm cycles[J]. Ann. Geophys., 2003, 21(5):1121-1140 [4] MILAN S E, BOAKES P D, HUBERT B. Response of the expanding/contracting polar cap to weak and strong solar wind driving:Implications for substorm onset[J]. J. Geophys. Res.:Space Phys., 2008, 113(A9). DOI: 10.1029/2008JA013340 [5] HUNG C C, GERMANY G. K-means and iterative selection algorithms in image segmentation[R]. IEEE Southeastcon, 2003 [6] LI X, RAMACHANDRAN R, HE M, et al. Comparing different thresholding algorithms for segmenting auroras[C]//International Conference on Information Technology:Coding and Computing. Proceedings. ITCC 2004. Las Vegas:IEEE, 2004:594-601 [7] SHI J, WU J, ANISETTI M, et al. An interval type-2 fuzzy active contour model for auroral oval segmentation[J]. Soft Comput., 2017, 21(9):2325-2345 [8] CAO C, NEWMAN T S, GERMANY G A. New shape-based auroral oval segmentation driven by LLS-RHT[J]. Pattern Recogn., 2009, 42(5):607-618 [9] YANG X, GAO X, LI J, et al. A shape-initialized and intensity-adaptive level set method for auroral oval segmentation[J]. Inform. Sci., 2014, 277:794-807 [10] LIU H, GAO X, HAN B, et al. An automatic MSRM method with a feedback based on shape information for auroral oval segmentation[C]//International Conference on Intelligent Science and Big Data Engineering. Heidelberg:Springer, 2013:748-755 [11] WANG Qian, MENG Qinghu, HU Zejun, et al. Extraction of auroral oval boundaries from UVI images:a new FLICM clustering-based method and its evaluation[J]. Adv. Polar Sci., 2011, 22(3):184-191 [12] DING G X, HE F, ZHANG X X, et al. A new auroral boundary determination algorithm based on observations from TIMED/GUVI and DMSP/SSUSI[J]. J. Geophys. Res.:Space Phys., 2017, 122(2):2162-2173 [13] YANG P, ZHOU Z, SHI H, et al. Auroral oval segmentation using dual level set based on local information[J]. Remote Sens. Lett., 2017, 8(12):1112-1121 [14] GARCIA-GARCIA A, ORTS-ESCOLANO S, OPREA S, et al. A review on deep learning techniques applied to semantic segmentation[R]. arXiv preprint arXiv:1704. 06857, 2017 [15] RONNEBERGER O, FISCHER P, BROX T. U-net:Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich:Springer, 2015:234-241 [16] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas:IEEE, 2016:770-778 [17] DROZDZAL M, VORONTSOV E, CHARTRAND G, et al. The Importance of Skip Connections in Biomedical Image Segmentation[M]//Deep Learning and Data Labeling for Medical Applications. Athens:Springer, 2016:179-187 [18] WANG Zihan, TONG Jizhou, ZOU Ziming, et al. "Auroral oval morphology extraction based on U-net from ultraviolet aurora observation" paper data. V1[DS]. NSSDC Space Science Article Data Repository. 21.86116. 7/01.99.00035 [19] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:a simple way to prevent neural networks from overfitting[J]. J. Mach. Learn. Res., 2014, 15(1):1929-1958 [20] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Trans. Syst. Man Cybern., 1979, 9(1):62-66 [21] KRINIDIS S, CHATZIS V. A robust fuzzy local information C-means clustering algorithm[J]. IEEE Trans. Image Process., 2010, 19(5):1328-1337 [22] RAJENDRAN A, DHANASEKARAN R. Fuzzy clustering and deformable model for tumor segmentation on MRI brain image:a combined approach[J]. Proced. Eng., 2012, 30:327-333
点击查看大图
计量
- 文章访问数: 416
- HTML全文浏览量: 93
- PDF下载量: 21
- 被引次数: 0