Abstract: In order to effectively classify the rural land, an object-oriented classification method is selected to extract the land classification information of drone aerial photography images. First, original drone-taking images are preprocessed, then by repeatedly performing segmentation tests on the study area, the optimal segmentation scale of each feature is selected, with which the images are segmented at different levels. And based on feature differences in feature vector, spectrum, shape, etc., the most suitable classification rules are established for the features on the optimal segmentation scale layer. Accordingly, the land use information of each layer can be extracted. Experimental results with 734 samples for accuracy verification show that the overall classification accuracy of multi-scale and multi-level segmentation classification reaches 84.20%, and the kappa coefficient is 0.8062, whereas the overall accuracy of single-scale segmentation classification is only 77.11%, and the kappa coefficient is 0.7214. It shows that the data used in this study and the classification accuracy of the categories inside the region are higher.

Key words: object-oriented classification, unmanned aerial vehicle image, multi-scale segmentation, land use