Classification of Surface Natural Resources based On U-NET and GF-1 Satellite Images

Natural resources are indispensable for human survival and the protection and management of surface natural resources become more and more important. At present, remote sensing and deep learning approaches are using in land use and land cover area. In this paper, a U-Net model is developed to classify surface natural resources using GF-1 satellite images by semantic segmentation. Experiment results indicate the effectiveness of the U-Net model to segment the surface nature resources, which can be applied in practice to support the management of nature resources.