Abstract:Based on Sentinel-2A remote sensing image data, this paper took Taihang Mountain Area as the research object to quantitatively analyze the different performance of five classification methods of maximum likelihood (ML), Bayes, support vector machine (SVM), decision tree, and random forest (RF) in the region under different feature combination modes, which adopting two strategies of pixel based and object-oriented classification. The results show that (1) the RF classifier based on pixel achieves the highest accuracy, while the overall accuracy of only using spectral features and using spectral, red edge and exponential features is 96.85% and 96.64%, respectively.(2) The addition of red edges and exponential features can have different degrees of impact on the classification accuracy of each classifier. Even if the overall accuracy of the pixel-based RF and object-oriented CART decision trees decreases, but the decline is about 0.5%. The accuracy of other classifiers has been improved.