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An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7700671/ https://www.ncbi.nlm.nih.gov/pubmed/33238513 http://dx.doi.org/10.3390/s20226699 |
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author | Sun, Fei Fang, Fang Wang, Run Wan, Bo Guo, Qinghua Li, Hong Wu, Xincai |
author_facet | Sun, Fei Fang, Fang Wang, Run Wan, Bo Guo, Qinghua Li, Hong Wu, Xincai |
author_sort | Sun, Fei |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7700671 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77006712020-11-30 An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images Sun, Fei Fang, Fang Wang, Run Wan, Bo Guo, Qinghua Li, Hong Wu, Xincai Sensors (Basel) Article 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. MDPI 2020-11-23 /pmc/articles/PMC7700671/ /pubmed/33238513 http://dx.doi.org/10.3390/s20226699 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sun, Fei Fang, Fang Wang, Run Wan, Bo Guo, Qinghua Li, Hong Wu, Xincai An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images |
title | An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images |
title_full | An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images |
title_fullStr | An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images |
title_full_unstemmed | An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images |
title_short | An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images |
title_sort | impartial semi-supervised learning strategy for imbalanced classification on vhr images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7700671/ https://www.ncbi.nlm.nih.gov/pubmed/33238513 http://dx.doi.org/10.3390/s20226699 |
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