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Semi-supervised classification framework of hyperspectral images based on the fusion evidence entropy

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Abstract

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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Acknowledgments

This research was jointly supported by the Chinese Ministry of Land and Resources Nonprofit Sector Research and Special Project Fund (2014110220202), The 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) and NSFC of China under Grant (41401403).

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Authors and Affiliations

  1. Key Laboratory of Mine Spatial Information Technologies, National Administration of Surveying, Mapping and Geo-information, Henan Polytechnic University, Jiaozuo, 454003, China

    Chunyang Wang

  2. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454003, China

    Chunyang Wang, Zhifang Xu, Shuangting Wang & Hebing Zhang

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  1. Chunyang Wang
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  2. Zhifang Xu
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  3. Shuangting Wang
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  4. Hebing Zhang
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Correspondence to Chunyang Wang.

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Wang, C., Xu, Z., Wang, S. et al. Semi-supervised classification framework of hyperspectral images based on the fusion evidence entropy. Multimed Tools Appl 77, 10615–10633 (2018). https://doi.org/10.1007/s11042-017-4686-x

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  • DOI: https://doi.org/10.1007/s11042-017-4686-x

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