Liguo Zhang^1,2, Zilin Tian¹, Yan Zhang^3,*, Tong Shuai⁴, Shuo Liang⁴, Zhuofei Wu⁵

Journal of New Media, Vol.4, No.2, pp. 59-71, 2022, DOI:10.32604/jnm.2022.029360

Abstract In recent years, deep learning algorithms have been popular in recognizing targets in synthetic aperture radar (SAR) images. However, due to the problem of overfitting, the performance of these models tends to worsen when just a small number of training data are available. In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition, in this paper, we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model (RBnet) for SAR image target recognition. In the RBnet, a… More >

Linshan Shen¹, Ye Tian^1,*, Liguo Zhang^1,2, Guisheng Yin¹, Tong Shuai³, Shuo Liang³, Zhuofei Wu⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 465-476, 2022, DOI:10.32604/cmc.2022.027488

Abstract The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing. However, the existing semi-supervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution, and its performance is mainly due to the two being in the same distribution state. When there is out-of-class data in unlabeled data, its performance will be affected. In practical applications, it is difficult to ensure that unlabeled data does not contain out-of-category data,… More >