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Article

An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images

by 1,2, 1,3, 1,4,*, 1,3, 5, 1,3 and 1,3
1
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
2
Academy of Computer, Huanggang Normal University, No. 146 Xinggang 2nd Road, Huanggang 438000, China
3
National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan 430078, China
4
Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430078, China
5
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(22), 6699; https://doi.org/10.3390/s20226699
Received: 27 October 2020 / Revised: 20 November 2020 / Accepted: 21 November 2020 / Published: 23 November 2020
(This article belongs to the Special Issue Remote Sensing Big Data for Improving the Urban Environment)
Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing. View Full-Text
Keywords: image classification; class imbalance; impartial semi-supervised learning strategy (ISS); extreme gradient boosting (XGB); very-high-resolution (VHR) image classification; class imbalance; impartial semi-supervised learning strategy (ISS); extreme gradient boosting (XGB); very-high-resolution (VHR)
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MDPI and ACS Style

Sun, F.; Fang, F.; Wang, R.; Wan, B.; Guo, Q.; Li, H.; Wu, X. An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images. Sensors 2020, 20, 6699. https://doi.org/10.3390/s20226699

AMA Style

Sun F, Fang F, Wang R, Wan B, Guo Q, Li H, Wu X. An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images. Sensors. 2020; 20(22):6699. https://doi.org/10.3390/s20226699

Chicago/Turabian Style

Sun, Fei, Fang Fang, Run Wang, Bo Wan, Qinghua Guo, Hong Li, and Xincai Wu. 2020. "An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images" Sensors 20, no. 22: 6699. https://doi.org/10.3390/s20226699

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