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Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data

机译:利用支持向量机和不平衡训练数据对遥感数据进行分类

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The classification of remote sensing data with imbalanced training data is addressed. The classification accuracy of a supervised method is affected by several factors, such as the classifier algorithm, the input data and the available training data. The use of an imbalanced training set, i.e., the number of training samples from one class is much smaller than from other classes, often results in low classification accuracies for the small classes. In the present study support vector machines (SVM) are trained with imbalanced training data. To handle the imbalanced training data, the training data are resampled (i.e., bagging) and a multiple classifier system, with SVM as base classifier, is generated. In addition to the classifier ensemble a single SVM is applied to the data, using the original balanced and the imbalanced training data sets. The results underline that the SVM classification is affected by imbalanced data sets, resulting in dominant lower classification accuracies for classes with fewer training data. Moreover the detailed accuracy assessment demonstrates that the proposed approach significantly improves the class accuracies achieved by a single SVM, which is trained on the whole imbalanced training data set.
机译:解决了具有不平衡训练数据的遥感数据分类问题。监督方法的分类准确性受多个因素影响,例如分类器算法,输入数据和可用的训练数据。使用不平衡的训练集,即一个类别的训练样本数量远少于其他类别的训练样本,通常会导致小类别的分类准确性较低。在本研究中,支持向量机(SVM)使用不平衡的训练数据进行训练。为了处理不平衡的训练数据,对训练数据进行重新采样(即装袋),并生成一个以SVM为基础分类器的多重分类器系统。除了分类器集合外,还使用原始的平衡和不平衡训练数据集将单个SVM应用于数据。结果表明,SVM分类受不平衡数据集的影响,导致训练数据较少的班级的较低分类准确性占主导地位。此外,详细的准确性评估表明,所提出的方法可显着提高单个SVM(在整个不平衡训练数据集上进行训练)所实现的班级准确性。

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