Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data

In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for the classification of hyperspectral images with limited training data. Our proposition is based on Rotation Forest (RoF), a classifying technique that has proved to be remarkably accurate in the context of h...

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Bibliographic Details
Published in:Information sciences 2021-10, Vol.575, p.611-638
Main Authors: Feng, Wei, Quan, Yinghui, Dauphin, Gabriel, Li, Qiang, Gao, Lianru, Huang, Wenjiang, Xia, Junshi, Zhu, Wentao, Xing, Mengdao
Format: Article
Language:English
Subjects:
Classification
Engineering Sciences
Ensemble margin
Environmental Sciences
Global Changes
Hyperspectral image
Rotation forest
Semi-supervised learning
Signal and Image processing
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Summary:In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for the classification of hyperspectral images with limited training data. Our proposition is based on Rotation Forest (RoF), a classifying technique that has proved to be remarkably accurate in the context of high-dimensional data. It is adapted to the semi-supervised context, by increasing the number of training instances in the learning stage, with high-quality unlabeled samples mined using ensemble margin. SMOTE is adopted to overcome the class imbalance problem. Out-Of-Bag (OOB) instances are used in a second phase to figure out the optimal number of samples to be added to the training set. Five ensemble methods and five semi-supervised methods are employed as comparisons. The results on three real hyperspectral remote sensing datasets demonstrate the effectiveness of the proposed method.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2021.06.059