U-net convolutional networks for mining land cover classification based on high-resolution UAV imagery

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Abstract

Mining activities are the leading cause of deforestation, land-use changes, and pollution. Land use/cover mapping inVietnam every five years is not useful to monitor land covers in mining areas, especially in the Central Highland region. It is necessary to equip managers with a better tool to monitor and map land cover using high-resolution images. Therefore, the authors proposed using the U-Net convolutional network for land-cover classification based on multispectral Unmanned aerial vehicle (UAV) image in a mining area of Daknong province, Vietnam. An area of 0.5kmx0.8km was used for training and testing seven U-Net models using seven optimizer function types. The final U-Net model can interpret six land cover types: (1) open-case mining lands, (2) old permanent croplands, (3) young permanent croplands, (4) grasslands, (5) bare soils, (6) water bodies. As a result, two models using Nadam and Adadelta optimizer function can be used to classify six land cover types with accuracy higher than 83%, especially in open-case mining lands and polluted streams flowed out from the mining areas. The trained U-Net models can potentially update new land cover types in other mining areas towards monitoring land cover changes in real-time in the future.

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APA

Giang, T. L., Dang, K. B., Le, Q. T., Nguyen, V. G., Tong, S. S., & Pham, V. M. (2020). U-net convolutional networks for mining land cover classification based on high-resolution UAV imagery. IEEE Access, 8, 186257–186273. https://doi.org/10.1109/ACCESS.2020.3030112

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