Radar image recognition is a hotspot in the field of remote sensing. Under the condition of sufficiently labeled samples, recognition algorithms can achieve good classification results. However, labeled samples are scarce and costly to obtain. Our major interest in this paper is how to use these unlabeled samples to improve the performance of a recognition algorithm in the case of limited labeled samples. This is a semi-supervised learning problem. However, unlike the existing semi-supervised learning methods, we do not use unlabeled samples directly and, instead, look for safe and reliable unlabeled samples before using them. In this paper, two new semi-supervised learning methods are proposed: a semi-supervised learning method based on fast search and density peaks (S2DP) and an iterative S2DP method (IS2DP). When the labeled samples satisfy a certain requirement, S2DP uses fast search and a density peak clustering method to detect reliable unlabeled samples based on the weighted kernel Fisher discriminant analysis (WKFDA). Then, a labeling method based on clustering information (LCI) is designed to label the unlabeled samples. When the labeled samples are insufficient, IS2DP is used to iteratively search for reliable unlabeled samples for semi-supervision. Then, these samples are added to the labeled samples to improve the recognition performance of S2DP. In the experiments, real radar images are used to verify the performance of our proposed algorithm in dealing with the scarcity of the labeled samples. In addition, our algorithm is compared against several semi-supervised deep learning methods with similar structures. Experimental results demonstrate that the proposed algorithm has better stability than these methods.
Bibliographical noteFunding Information:
This work was supported by the National Natural Science Foundation of China (61771027; 61071139; 61471019; 61171122; 61501011; 61671035), the Scientific Research Foundation of Guangxi Education Department (KY2015LX444), the Scientific Research and Technology Development Project of Wuzhou, Guangxi, China (201402205), the Guangxi Science and Technology Project (Guike AB16380273), and the Research and Practice on Teaching Reform of Web Page Making and Design Based on the Platform of “E-Commerce Pioneer Park” (Guijiao Zhicheng 41). Professor A. Hussain was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) Grant no. EP/M026981/1. E. Yang was supported in part under the RSE-NNSFC Joint Project (2017-2019), grant number 6161101383, with China University of Petroleum (Huadong). H. Zhou was supported by UK EPSRC under Grant EP/N011074/1, Royal Society-Newton Advanced Fellowship under Grant NA160342, and European Union’s Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie Grant agreement no. 720325.
© 2019 Fei Gao et al.
Copyright 2019 Elsevier B.V., All rights reserved.
ASJC Scopus subject areas
- Computer Science(all)