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The tagged data is used to train the Softmax classifier and provide the initial K-means clustering center for the untagged data. The nonsubsampling contourlet layer is used to replace the first convolutional layer of the full convolutional neural network to extract multi-scale depth features, and the nonsubsampling contourlet full convolutional neural network is constructed. The network can extract multi-scale information of the images to be classified, and extract more discriminative deep image features. In addition, the parameters of the nonsubsampled contourlet layers are pre-set and do not require network training. The proposed method has higher classification accuracy than the contrast method on polarimetric SAR images using the nonsubsampled contourlet full convolutional neural network. <\/jats:p>","DOI":"10.1142\/s0218126624500567","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T03:46:06Z","timestamp":1689824766000},"source":"Crossref","is-referenced-by-count":1,"title":["An Image Classification Method Based on Semi-Supervised Classification Learning and Convolutional Neural Networks"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6208-1326","authenticated-orcid":false,"given":"Liyan","family":"Shi","sequence":"first","affiliation":[{"name":"The Open University of Henan, School of Information Engineering and Artificial Intelligence, Zhengzhou, Henan 450046, P. R. 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