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Open AccessArticle

Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance

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School of Geography and of Information Engineering, China University of Geosciences, No. 388 Lumo Road, Wuhan 430074, China
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National Engineering Research Center of Geographic Information System, Wuhan 430074, China
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State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(7), 315; https://doi.org/10.3390/ijgi8070315
Received: 31 May 2019 / Revised: 11 July 2019 / Accepted: 20 July 2019 / Published: 23 July 2019
Imbalanced learning is a methodological challenge in remote sensing communities, especially in complex areas where the spectral similarity exists between land covers. Obtaining high-confidence classification results for imbalanced class issues is highly important in practice. In this paper, extreme gradient boosting (XGB), a novel tree-based ensemble system, is employed to classify the land cover types in Very-high resolution (VHR) images with imbalanced training data. We introduce an extended margin criterion and disagreement performance to evaluate the efficiency of XGB in imbalanced learning situations and examine the effect of minority class spectral separability on model performance. The results suggest that the uncertainty of XGB associated with correct classification is stable. The average probability-based margin of correct classification provided by XGB is 0.82, which is about 46.30% higher than that by random forest (RF) method (0.56). Moreover, the performance uncertainty of XGB is insensitive to spectral separability after the sample imbalance reached a certain level (minority:majority > 10:100). The impact of sample imbalance on the minority class is also related to its spectral separability, and XGB performs better than RF in terms of user accuracy for the minority class with imperfect separability. The disagreement components of XGB are better and more stable than RF with imbalanced samples, especially for complex areas with more types. In addition, appropriate sample imbalance helps to improve the trade-off between the recognition accuracy of XGB and the sample cost. According to our analysis, this margin-based uncertainty assessment and disagreement performance can help users identify the confidence level and error component in similar classification performance (overall, producer, and user accuracies). View Full-Text
Keywords: gradient boosting trees; margin; class imbalance; very-high resolution (VHR) remote sensing; land cover classification; disagreement performance gradient boosting trees; margin; class imbalance; very-high resolution (VHR) remote sensing; land cover classification; disagreement performance
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Sun, F.; Wang, R.; Wan, B.; Su, Y.; Guo, Q.; Huang, Y.; Wu, X. Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance. ISPRS Int. J. Geo-Inf. 2019, 8, 315.

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