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Hierarchical Decision Tree for Change Detection Using High Resolution Remote Sensing Images

  • Zhiwei XieEmail author
  • Min Wang
  • Yaohui Han
  • Dayong Yang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)

Abstract

In order to improve the classification accuracy of high resolution change detection, the key technologies such as segmentation scale determination, features selection and classifier use are studied, and a change detection method using hierarchical decision tree is proposed. Firstly, fractal net evolution approach was used to obtain image objects, and the optimal scales of vegetation, water, and man-made objects were determined by evaluation index based on classification accuracy. Second, the feature spaces of man-made, water and vegetation objects are constructed. Then, the hierarchical decision tree classification method with the optimal segmentation scales is applied to multi-temporal high resolution remote sensing images. Finally, change detection was implemented by comparing the multi-temporal classification results. The multi-temporal high resolution remote sensing images in Wuhan Lujiazhuang were chosen as the experimental data. The experiments show that the method is effective.

Keywords

Image objects Optimal segmentation scale Ratio index Hierarchical decision tree Change detection 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Transportation EngineeringShenyang Jianzhu UniversityShenyangChina
  2. 2.Faculty of Geosciences and Environmental EngineeringSouthwest Jiaotong UniversityChengduChina

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