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Comparison of object-oriented remote sensing image classification based on different decision trees in forest area.

Li Ping Chen1, Yu Jun Sun1

  • 1State Forestry Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China.

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|December 26, 2018
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Summary

Geographic Object-Based Image Analysis (GEOBIA) improves forest classification accuracy using decision trees. The C5.0 decision tree achieved the highest accuracy, outperforming the kNN method for high-resolution remote sensing data.

Keywords:
C5.0 decision treeclassificationdecision treeremote sensing

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Area of Science:

  • Remote Sensing
  • Image Processing
  • Geographic Information Systems (GIS)

Background:

  • Increasing volumes of high-resolution remote sensing data necessitate advanced classification techniques.
  • Geographic Object-Based Image Analysis (GEOBIA) is a key approach for processing this data.
  • Improving classification accuracy and efficiency is crucial for image analysis.

Purpose of the Study:

  • To analyze the efficiency of C5.0, C4.5, and CART decision trees for object-oriented classification of forest areas.
  • To compare the accuracy of these decision tree methods with the kNN method.
  • To evaluate the effectiveness of decision tree algorithms in improving forest species classification.

Main Methods:

  • Multiscale segmentation of QuickBird imagery using eCognition software, identifying optimal scales of 90 and 40.
  • Extraction of 21 spectral, textural, and shape features for different vegetation types at the 40 scale.
  • Application of C5.0, C4.5, and CART decision tree algorithms for knowledge mining and rule establishment.
  • Classification of vegetation areas and comparison of accuracy with the kNN method.

Main Results:

  • Decision tree methods demonstrated higher classification accuracy than the traditional kNN method.
  • The C5.0 decision tree method yielded the best results, with 90.0% overall accuracy and a 0.87 Kappa coefficient.
  • The Boosting algorithm within the C5.0 decision tree provided the most significant accuracy improvement.

Conclusions:

  • Decision tree algorithms effectively enhance the accuracy of forest species classification.
  • GEOBIA, combined with decision tree methods like C5.0, offers a robust solution for high-resolution remote sensing image classification.
  • The C5.0 algorithm, particularly with its Boosting function, is highly recommended for accurate and efficient forest classification.