Journal of the Indian Society of Remote Sensing

, Volume 47, Issue 1, pp 113–123 | Cite as

Ensemble Classification of Hyperspectral Images by Integrating Spectral and Texture Features

  • Haiyong DingEmail author
  • Xiaoying Wang
  • Yucun Wang
  • Haibin Luo
Research Article


Hyperspectral images provide abundant spectral information of the land surface materials, which make it possible to distinguish those materials with subtle difference. Researches about improvement in classification accuracy of hyperspectral images have been conducted from two aspects. The first one is trying to integrating other features such as texture features, geometry features and geographical information. The second one focuses on the employment of advance classifiers such as random forest classifier, support vector machine classifier, decision tree classifier. This paper demonstrated a recent study about the ensemble classification of hyperspectral image by integrating spectral features and texture features. Morphology texture features were extracted from the principal components of the hyperspectral bands. Multi-size of structure elements was used to get the morphology texture images by implementing the closing and opening operation. Texture features extracted from the gray-level co-occurrence matrix were also utilized to classify the hyperspectral images. Four classifiers were trained using spectral features, texture features, combined spectral and texture features, respectively. It was found that a single feature induced relatively poor classification accuracy in all of the four classifiers. Integrated spectral–texture features generated improved classification results. On the other hand, ensemble classification could produce much better classification effects compared with a single classifier. Further works will focus on the classification performance of other features such as wavelet texture feature and context information.


Ensemble classification Morphological texture feature Gray-level co-occurrence matrix Hyperspectral images 



This work was supported by the National Natural Science Foundation of China under Grants 41571350 and 41301400. It was also supported partially by the Innovation and Entrepreneurship Training Program for Jiangsu College Students under Grants 201510300067.


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Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  • Haiyong Ding
    • 1
    Email author
  • Xiaoying Wang
    • 1
  • Yucun Wang
    • 2
  • Haibin Luo
    • 1
  1. 1.School of Remote Sensing & Geomatics EngineeringNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  2. 2.College of Information Science and EngineeringShandong Agricultural UniversityTaianPeople’s Republic of China

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