Application of Object-Oriented Classification Based on ESP2 in High-Speed Railway Line Extraction
The ESP2 tool in eCognition software is used in combination with the GF-2 images as the original data to predict the optimal segmentation scale parameters of the image,and the high-speed railway lines are extracted through three object-ori-ented classification methods,i. e.,k-nearest neighbor,classi-fication and regression tree,and support vector machine. Five indicators of overall accuracy,Kappa coefficient,completion rate,correct rate and extraction quality are introduced to evaluate the accuracy of the extracted high-speed railway lines. The experimental results show that the five mentioned extraction indexes of the three methods are all above 0.9,which indicates that the object-oriented classification method is feasible in the field of high-speed railway line extraction.
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