Abstract: With the wide application of object-oriented image analysis, image segmentation plays an important role in remote sensing image processing. At present, many image segmentation algorithms are based on region merging method, but these methods generally face the problem of limited feature scale, relying on a single optical image feature and subjective parameter setting, which limits the segmentation effect. To solve this problem, this paper proposes a machine learning region merging method that takes into account the elevation feature merging strategy. In this paper, the machine learning model based on random forest (RF) is used to assist the elevation feature merging strategy, and the region merging classifier is constructed by calculating the feature matrix of the adjacent region as the input feature variable. Transform the region merging problem into a classification problem of 0 and 1. Experimental results show that the region merging algorithm with 0.5 m spatial resolution elevation features achieves excellent segmentation results, with F1, accuracy, recall and crossover ratio reaching 90.5%, 89.98%, 91.02% and 82.64%, respectively. Compared with no elevation features, the proposed algorithm effectively improves segmentation accuracy. It is increased by about 3.4%, 6.8%, 1.1% and 6.2% respectively. Meanwhile, the importance of elevation features reached 32.5%, which is about 7% higher than that of optical features.

Key words: machine learning, elevation characteristics, region merging, image segmentation, scale variable