Mechanical damaged surface tends to cause soil erosion, secondary geological hazards and other ecological environment problems, but there is still a lack of effective extraction methods based on remote sensing images. Based on the GF-2 remote sensing image, the authors studied the object-oriented extraction method based on texture features in Tanglangchuan watershed with densely distributed mechanical damage surface. According to the seven types of features, the classification rules were established. On the basis of the optimal scale segmentation, the decision tree A based on spectral features and the decision tree B based on "spectral + texture" features are classified in object-oriented way. Precision evaluation and analysis show that, compared with the traditional supervised classification method and the spectral-based object-oriented classification method, the classification method improves the Kappa coefficient and the total accuracy to 0.82 and 86.25%, respectively, and also effectively improves the extraction accuracy of mechanical damage surface.
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