Semisupervised classification of hyperspectral images based on tri-training algorithm with enhanced diversity

Ying Cui; Guojiao Song; Xueting Wang; Zhongjun Lu; Liguo Wang

doi:10.1117/1.JRS.11.045006

27 October 2017 Semisupervised classification of hyperspectral images based on tri-training algorithm with enhanced diversity

Ying Cui, Guojiao Song, Xueting Wang, Zhongjun Lu, Liguo Wang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 045006 (October 2017). https://doi.org/10.1117/1.JRS.11.045006

Abstract

Hyperspectral image classification faces a serious challenge due to the high dimension of hyperspectral data and limited labeled samples. Tri-training algorithm is a widely used semisupervised classification method, but the algorithm lacks significant diversity among the classifiers when the number of initial label samples is limited. A semisupervised classification method for hyperspectral data based on tri-training is proposed. It combines different classifiers and stratified sampling based on labeled class to increase classifier diversity and avoid classifier performance deterioration. Performance comparison between the proposed algorithm and tri-training algorithm was made through experiments. The proposed algorithm improved the overall accuracy and Kappa coefficient by 1.37% to 6.84% and 0.0096 to 0.0808, respectively, and the results showed that the effectiveness of the algorithm is verified. Moreover, the algorithm can also get better performance when the number of samples is small.

Citation Download Citation

Ying Cui, Guojiao Song, Xueting Wang, Zhongjun Lu, and Liguo Wang "Semisupervised classification of hyperspectral images based on tri-training algorithm with enhanced diversity," Journal of Applied Remote Sensing 11(4), 045006 (27 October 2017). https://doi.org/10.1117/1.JRS.11.045006

Received: 15 April 2017; Accepted: 21 September 2017; Published: 27 October 2017

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