The use of the single machine learning classifier for high-resolution remote sensing (RS) image classification makes it difficult to improve the accuracy of classification results. To fully utilize the advantages of different classifiers for different types of ground objects, based on the Dempster–Shafer (DS) evidence theory, we propose a multi-classifier fusion method for classification of high-resolution RS images. Six machine learning classifiers: support vector machines, k-nearest neighbor, random forest, artificial neural network, classification and regression tree, and the C5.0 decision tree were selected for application in the fusion of multiple classifiers. We calculated a classifier difference index based on the accuracy and difference of the classification results of the base classifiers. Base classifiers with large differences were selected to perform integration based on the DS evidence theory. We also improved the classical DS evidence theory. First, based on the classification validity of the base classifier for different ground objects, the classification probability value of the base classifier for different samples was weight optimized. Then different fusion methods were selected according to the classification conflict coefficients between base classifiers. The results reveal that the overall accuracy and kappa coefficients of the fusion classifier are significantly better than those of the base classifier. The producer’s accuracy and user’s accuracy of the fusion results based on the improved DS evidence theory were higher than those of the fusion results based on the classical DS evidence theory.

中文翻译：

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基于多分类器融合和改进的Dempster-Shafer证据理论的高分辨率遥感影像土地覆盖分类

将单个机器学习分类器用于高分辨率遥感（RS）图像分类，使得很难提高分类结果的准确性。为了充分利用针对不同类型地面物体的不同分类器的优势，基于Dempster-Shafer（DS）证据理论，我们提出了一种用于高分辨率RS图像分类的多分类器融合方法。选择了六个机器学习分类器：支持向量机，k最近邻，随机森林，人工神经网络，分类和回归树以及C5.0决策树，以用于多个分类器的融合。我们根据基础分类器分类结果的准确性和差异来计算分类器差异指数。根据DS证据理论，选择差异较大的基础分类器进行整合。我们还改进了经典的DS证据理论。首先，基于基础分类器对不同地面物体的分类有效性，对不同样本基础分类器的分类概率值进行权重优化。然后根据基本分类器之间的分类冲突系数选择不同的融合方法。结果表明，融合分类器的整体准确性和kappa系数明显优于基础分类器。基于改进DS证据理论的融合结果的生产者准确度和用户准确度均高于基于经典DS证据理论的融合结果的产生者准确度和用户准确度。