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With the rapid development of agricultural informationization, the demand for accuracy and reality of regional soil and water resources information data becomes higher and higher. The progress of remote sensing technology makes the selectable data source richer. High spatial resolution images contain rich shape and texture information which are widely used in soil and water resources survey, while traditional image classification method cannot satisfy the requirement any more.Because of this, unmanned aerial vehicle (UAV) images were used as experimental objects, and the image objectoriented classification method based on optimal segmentation scale and decision tree was proposed. Firstly, a segmentation quality function was established based on internal homogeneity and heterogeneity of images, and the optimal segmentation scale was obtained according to this function. Then, optimal segmentation scale evaluation model based on spectral and area information was proposed to evaluate segmentation result. Lastly, soil and water resource information classification was completed by introducing decision tree rule mechanism, and compared with the maximum likelihood classification results. The experimental results showed that the segmentation quality function can obtain optimal segmentation scale accurately, and avoid the subjectivity of manual segmentation. The overall accuracy is 86.78% and compared with 77.59% of maximum likelihood classification method has a great improvement in classification accuracy.