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.

中文翻译：

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n VHR图像不均衡分类的公正半监督学习策略

在基于遥感影像的土地利用和土地覆盖分类中，学习失衡是一个普遍的问题。学习不平衡会导致分类准确性降低，甚至导致少数群体的遗漏。在本文中，提出了一种基于极端梯度增强（ISS-XGB）的公正半监督学习策略，以对数据不平衡的超高分辨率（VHR）图像进行分类。ISS-XGB通过使用几个半监督分类器来解决多分类问题。它首先使用多组未标记数据来消除训练样本的不平衡，然后利用基于梯度增强的回归来模拟带有正样本和未标记样本的目标类别。在这项研究中，对八个具有不同失衡状况的研究区域进行了实验。结果表明，与最常用的分类方法（即随机森林（RF），XGB，多层感知器（MLP）和支持向量机（SVM））相比，ISS-XGB具有可比但更稳定的性能，积极的和未标记的学习（PU学习）方法（PU-BP和PU-SVM），以及典型的基于合成样本的不平衡学习方法。尤其是在极端不平衡的情况下，ISS-XGB可以为少数族裔提供高精度，而不会损失总体性能（平均总体准确性达到85.92％）。所提出的策略在解决遥感分类不平衡问题方面具有很大的潜力。正面和无标签学习（PU-Learning）方法（PU-BP和PU-SVM），以及典型的基于合成样本的不平衡学习方法。尤其是在极端不平衡的情况下，ISS-XGB可以为少数族裔提供高精度，而不会损失总体性能（平均总体准确性达到85.92％）。所提出的策略在解决遥感分类不平衡问题方面具有很大的潜力。正面和无标签学习（PU-Learning）方法（PU-BP和PU-SVM），以及典型的基于合成样本的不平衡学习方法。尤其是在极端不平衡的情况下，ISS-XGB可以为少数族裔提供高精度，而不会损失总体性能（平均总体准确性达到85.92％）。所提出的策略在解决遥感分类不平衡问题方面具有很大的潜力。