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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Tuan Linh Giang Kinh Bac Dang Quang Toan Le Vu Giang Nguyen Si Son Tong Van-Manh Pham |
| Abstract | Mining activities are the leading cause of deforestation, land-use changes, and pollution. Land use/cover mapping in Vietnam 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. |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2020.3030112 |
| Journal | IEEE Access |
| Volume Number | 8 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2020-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Segmentation Permanent Cropland U-net Convolutional Network Unmanned Aerial Vehicle Deep Learning Daknong |
| Content Type | Text |
| Resource Type | Article |
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