期刊
MULTIMEDIA TOOLS AND APPLICATIONS
卷 77, 期 9, 页码 10615-10633出版社
SPRINGER
DOI: 10.1007/s11042-017-4686-x
关键词
Hyperspectral image; Image classification; D-S evidence theory; Support vector machine; Semi-supervised learning; Probability output of the multi-class support vector machine; Evidence entropy
类别
资金
- Chinese Ministry of Land and Resources Nonprofit Sector Research and Special Project Fund [2014110220202]
- Open Program of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Henan Province [2016A002]
- Henan Polytechnic University Doctoral Fund [B2016-13]
- Open Fund of the Key Laboratory of Mine Spatial Information Technologies of the National Administration of Surveying, Mapping, and Geoinformation [KLM201407]
- NSFC of China [41401403]
Increasing attention is being paid to the classification of ground objects using hyperspectral spectrometer images. A key challenge of most hyperspectral classifications is the cost of training samples. It is difficult to acquire enough effective marked label sets using classification model frameworks. In this paper, a semi-supervised classification framework of hyperspectral images is proposed to better solve problems associated with hyperspectral image classification. The proposed method is based on an iteration process, making full use of the small amount of labeled data in a sample set. In addition, a new unlabeled data trainer in the self-training semi-supervised learning framework is explored and implemented by estimating the fusion evidence entropy of unlabeled samples using the minimum trust evaluation and maximum uncertainty. Finally, we employ different machine learning classification methods to compare the classification performance of different hyperspectral images. The experimental results indicate that the proposed approach outperforms traditional state-of-the-art methods in terms of low classification errors and better classification charts using few labeled samples.
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