Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV)

Abstract

Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation.

Keywords

• UAV
• deep learning
• remote sensing interpretation