Chrome Extension
WeChat Mini Program
Use on ChatGLM

Image Classification of Solar Radio Spectrum Based on Deep Learning

2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)(2020)

Cited 1|Views3
Abstract
Aiming at the problems that traditional image denoising methods cannot well filter the background noise of solar radio spectrum images, and the training sample data is small and unbalanced, it is proposed to segment the features and background of the spectrum image through Gaussian filtering and image binarization. Then use the morphological closed operation to enhance the feature; through the combination of image transformation and random indexing, the problem of uneven distribution of various samples in the solar radio spectrum database is solved. By designing the structure of the convolutional neural network, the classification model can better extract image features and improve the classification accuracy. Experimental results show that the convolutional neural network combined with image preprocessing has achieved an average TPR value of 97.97% on the solar radio spectrum data set, which is better than the existing research results and has application value for the automatic classification of solar radio spectrum images.
More
Translated text
Key words
sample enhancement,image preprocessing,spectrum image classification,convolutional neural networkl,solar radio burst
求助PDF
上传PDF
Bibtex
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined