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Journal of Mountain Science

, Volume 10, Issue 5, pp 768–776 | Cite as

Combining spectral with texture features into object-oriented classification in mountainous terrain using advanced land observing satellite image

  • En-qin LiuEmail author
  • Wan-cun Zhou
  • Jie-ming Zhou
  • Huai-yong Shao
  • Xin Yang
Article

Abstract

Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS (Advanced Land Observing Satellite) in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures (such as shadow). At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM (Gray Level Co-occurrence Matrix) were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification (MLC). Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and 0.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.

Keywords

Texture features Object-oriented classification Land use Mountain ALOS 

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Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • En-qin Liu
    • 1
    • 2
    • 3
    Email author
  • Wan-cun Zhou
    • 2
  • Jie-ming Zhou
    • 4
  • Huai-yong Shao
    • 1
  • Xin Yang
    • 1
  1. 1.Key Laboratory of Geo-special Information Technology, Ministry of Land and ResourcesChengdu University of TechnologyChengduChina
  2. 2.Chengdu Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.The Faculty of Geography Resources SciensesSichuan Normal UniversityChengduChina

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