Open AccessImage Recognition Technology of Synthetic Aperture Radar
Author(s) -
Rongxia Duan,
Liu Jiaru,
Baocai Xu,
Tianchen Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1744/4/042182
Subject(s) - synthetic aperture radar , artificial intelligence , computer science , filter (signal processing) , inverse synthetic aperture radar , computer vision , radar , deep learning , radar imaging , artificial neural network , path (computing) , pattern recognition (psychology) , telecommunications , programming language
The rapid development of artificial intelligence in recent years, especially the rise of deep learning, provides a new path for radar target recognition. This paper first introduces the development of synthetic aperture radar and deep learning, and then analyzes the challenges and difficulties of deep neural networks. Then the self-encoder is used for target recognition. The model uses self-encoder to realize feature selection and data Manacor to classify the final object. In this paper, the advantages and disadvantages of the model are analyzed, and the improvement ideas are put forward. Finally, the original synthetic aperture radar image is processed by Median filter, mean filter and Gaussian filter. After comparison and analysis, it is concluded that the filtering effect is best when the Median filter size is 22.