AstroSer: Leveraging Deep Learning for Efficient Content-based Retrieval in Massive Solar-observation Images

Rapid and proficient data retrieval is an essential component of modern astronomical research. In this paper, we address the challenge of retrieving astronomical image content by leveraging state-of-the-art deep learning techniques. We have designed a retrieval model, HybridVR, that integrates the c...

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Bibliographic Details
Published in:Publications of the Astronomical Society of the Pacific 2023-12, Vol.135 (1054), p.124501
Main Authors: Wu, Shichao, Liu, Yingbo, Yang, Lei, Liu, Xiaoying, Li, Xingxu, Xiang, Yongyuan, Gong, Yunyu
Format: Article
Language:English
Subjects:
Active sun
Adaptability
Astronomy
Astronomy data analysis
Astronomy software
Astrostatistics techniques
Computational methods
Data management
General relativity
Information retrieval
Solar activity
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Summary:Rapid and proficient data retrieval is an essential component of modern astronomical research. In this paper, we address the challenge of retrieving astronomical image content by leveraging state-of-the-art deep learning techniques. We have designed a retrieval model, HybridVR, that integrates the capabilities of the deep learning models ResNet50 and VGG16 and have used it to extract key features of solar activity and solar environmental characteristics from observed images. This model enables efficient image matching and allows for content-based image retrieval (CBIR). Experimental results demonstrate that the model can achieve up to 98% similarity during CBIR while exhibiting adaptability and scalability. Our work has implications for astronomical research, data management, and education, and it can contribute to optimizing the utilization of astronomical image data. It also serves as a useful example of the application of deep learning technology in the field of astronomy.
ISSN:0004-6280
1538-3873
DOI:10.1088/1538-3873/ad0e7e