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Dynamic ensemble algorithm of SMOTE and rotation forest for imbalanced hyperspectral remote sensing classification

  • role： First author第一作者
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

    Research Institute of Advanced Remote Sensing Technology, Xidian University, Xi’an 710071, China

  • Email：yptong@stu.xidian.edu.cn
  • Introduction：童莹萍，研究方向为遥感图像处理。E-mail：yptong@stu.xidian.edu.cn
TONG Yingping12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

    Research Institute of Advanced Remote Sensing Technology, Xidian University, Xi’an 710071, China

  • Email：wfeng@xidian.edu.cn
  • Introduction：冯伟，研究方向为遥感图像处理、机器学习。E-mail：wfeng@xidian.edu.cn
FENG Wei12*,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

    Research Institute of Advanced Remote Sensing Technology, Xidian University, Xi’an 710071, China

SONG Yijia12,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

    Research Institute of Advanced Remote Sensing Technology, Xidian University, Xi’an 710071, China

QUAN Yinghui12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

HUANG Wenjiang3,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

GAO Lianru3,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

    Research Institute of Advanced Remote Sensing Technology, Xidian University, Xi’an 710071, China

ZHU Wentao12,  
  • Affiliation：

    Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710071, China

XING Mengdao4

resumen

Rotation Forest (RoF), a powerful ensemble classifier, has obtained many successful applications in hyperspectral image classification. However, the data often has the problem of class imbalance. Consequently, the traditional RoF algorithm focuses on identifying the classes with majority samples, ignoring the accuracy of minority samples. The SMOTE (Synthetic Minority Oversampling Technique) algorithm increases the number of minority samples by simulating the way of generating new samples, thereby achieving the effect of balancing the categories of the data set. However, the SMOTE algorithm is mainly used in the data preprocessing stage and has the risk of increasing artificial noise when dealing with multi-class problems. Therefore, a novel dynamic ensemble algorithm based on SMOTE and RoF is proposed in this work to increase the classification accuracy of the multi-class imbalanced hyperspectral data. The proposed algorithm uses a dynamic sampling factor technology to merge the class distribution optimization with the base classifier. This algorithm not only realizes the adaptive generation of class balance data set but also reduces the influence of noise on the base classifier. In this experiment, three public hyperspectral images are used to test the performance of the algorithm, They are Indian Pines, Salinas and Pavia University. Four comparison algorithms are also selected, including random forest, traditional RoF, RoF algorithm with random oversampling, and SMOTE data preprocessing. The overall accuracy, average accuracy, F-measure, Gmean, minimum recall rate, ensemble classifier diversity, model training time, and McNemar test are the algorithm evaluation criteria. The experimental results demonstrate the effectiveness of the proposed method. The novel method not only obtains obvious classification advantages but also increases the recognition accuracy of minority samples while maintaining the overall classification accuracy of the data.

palabra clave

ensemble learning;imbalanced classification;rotation forest;SMOTE;dynamic sampling

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