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Automatic Classification of Remote Sensing Images Using Multiple Classifier Systems

机译：使用多个分类器系统自动分类遥感影像

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摘要

It is a challenge to obtain accurate result in remote sensing images classification, which is affected by many factors. In this paper, aiming at correctly identifying land use types reflected in remote sensing images, support vector machine, maximum likelihood classifier, backpropagation neural network, fuzzy c-means, and minimum distance classifier were combined to construct three multiple classifier systems (MCSs). Two MCSs were implemented, namely, comparative major voting (CMV) and Bayesian average (BA). One method called WA-AHP was proposed, which introduced analytic hierarchy process into MCS. Classification results of base classifiers and MCSs were compared with the ground truth map. Accuracy indicators were computed and receiver operating characteristic curves were illustrated, so as to evaluate the performance of MCSs. Experimental results show that employing MCSs can increase classification accuracy significantly, compared with base classifiers. From the accuracy evaluation result and visual check, the best MCS is WA-AHP with overall accuracy of 94.2%, which overmatches BA and rivals CMV in this paper. The producer's accuracy of each land use type proves the good performance of WA-AHP. Therefore, we can draw the conclusion that MCS is superior to base classifiers in remote sensing image classification, and WA-AHP is an efficient MCS.
机译：在许多因素影响下，在遥感影像分类中获得准确的结果是一个挑战。本文针对正确识别遥感影像中反映的土地利用类型，将支持向量机，最大似然分类器，反向传播神经网络，模糊c均值和最小距离分类器相结合，构建了三个多重分类器系统（MCS）。实施了两个MCS，即比较主要投票（CMV）和贝叶斯平均（BA）。提出了一种称为WA-AHP的方法，该方法将层次分析过程引入了MCS。将基础分类器和MCS的分类结果与地面真相图进行比较。计算了精度指标并说明了接收机的工作特性曲线，以评估MCS的性能。实验结果表明，与基本分类器相比，使用MCS可以显着提高分类精度。从准确性评估结果和目视检查来看，最佳MCS是WA-AHP，总体准确性为94.2％，与本文中的BA和竞争对手CMV相比过高。生产商对每种土地利用类型的准确性证明了WA-AHP的良好性能。因此，我们可以得出结论：在遥感图像分类中，MCS优于基本分类器，而WA-AHP是一种有效的MCS。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第19期|954086.1-954086.10|共10页
  • 作者

    Yang Bin; Cao Chunxiang; Xing Ying; Li Xiaowen;

  • 作者单位

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China|Univ Chinese Acad Sci, Beijing 100049, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;

    Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;

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