Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images

Filaments are a type of wide-existing astronomical structure. It is a challenge to separate filaments from radio astronomical images, because their radiation is usually weak. What is more, filaments often mix with bright objects, e.g., stars, which makes it difficult to separate them. In order to ex...

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Bibliographic Details
Published inAdvances in Astronomy Vol. 2019; no. 2019; pp. 1 - 11
Main Authors Duan, Fu-qing, Hu, D., Liang, T. H., Yu, X. C., Tian, W. W., Zhang, M. F., Wang, B. Y., Liu, W., Zhu, M., Wu, D.
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2019
Hindawi
John Wiley & Sons, Inc
Hindawi Limited
Subjects
Algorithms
Analysis
Astronomy
Celestial bodies
Classification
Decomposition
Dictionaries
Earth science
Equipment and supplies
Filaments
Image processing
Mathematical morphology
Morphology
Noise
Pattern recognition
Radiation
Radio
Remote sensing
Signal processing
Sparsity
Watersheds
Wavelet transforms
China
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Summary:Filaments are a type of wide-existing astronomical structure. It is a challenge to separate filaments from radio astronomical images, because their radiation is usually weak. What is more, filaments often mix with bright objects, e.g., stars, which makes it difficult to separate them. In order to extract filaments, A. Men’shchikov proposed a method “getfilaments” to find filaments automatically. However, the algorithm removed tiny structures by counting connected pixels number simply. Removing tiny structures based on local information might remove some part of the filaments because filaments in radio astronomical image are usually weak. In order to solve this problem, we applied morphology components analysis (MCA) to process each singe spatial scale image and proposed a filaments extraction algorithm based on MCA. MCA uses a dictionary whose elements can be wavelet translation function, curvelet translation function, or ridgelet translation function to decompose images. Different selection of elements in the dictionary can get different morphology components of the spatial scale image. By using MCA, we can get line structure, gauss sources, and other structures in spatial scale images and exclude the components that are not related to filaments. Experimental results showed that our proposed method based on MCA is effective in extracting filaments from real radio astronomical images, and images processed by our method have higher peak signal-to-noise ratio (PSNR).
ISSN:1687-7969
1687-7977
DOI:10.1155/2019/2397536