Hyperspectral image classification based on improved M-training algorithm

被引：0

作者：

Cui Y. ^{[1
,2
]}

Wang X. ^{[1
]}

Lu Z. ^{[2
]}

Wang L. ^{[1
]}

机构：

[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin

[2] Remote Sensing Technology Center, Heilongjiang Academy of Agricultural Science, Harbin

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2018年 / 39卷 / 10期

关键词：

Error rate weighting; Hyper-spectral image; Image processing; KNN classifier; M-training algorithm; RF classifier; Semi-supervised classification; SVM;

D O I：

10.11990/jheu.201707022

中图分类号：

学科分类号：

摘要：

To solve the problem of limited hyperspectral image classification of labeled samples, a modified M-training algorithm was applied to such classification. The algorithm employs two Support Vector Machines (SVMs), one K Nearest Neighbor (KNN), and one Random Forest (RF) classifier to enhance the diversity of classifiers, so as to improve the traditional algorithm of M-training. Taking the impact of a large number of unlabeled samples into account, the error rate of labeled and unlabeled samples was weighted as the limiting condition to updating the set of labeled samples, effectively enlarging the labeled set. The results showed that, compared with the traditional M-training algorithm, the proposed algorithm improves both overall classification accuracy and the Kappa coefficient by 1.85%~12.10% and 0.021 5~0.141 3, respectively, verifying the effectiveness of the improved algorithm. © 2018, Editorial Department of Journal of HEU. All right reserved.

引用

页码：1688 / 1694

页数：6

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