Classification of Surface Natural Resources Based on HR-Net and DEM.

With the vigorous advocacy of the concept of green development, the protection and management of natural resources become more and more important. It is of great significance to study the classification of surface natural resources by remote sensing. In this paper, a high-resolution net (HR-Net) model is used to classify surface natural resources by Gaofen-1 (GF-1) satellite images and digital elevation model (DEM) data. First of all, we obtained the GF-1 satellite images, DEM data and census data of geographical conditions of the study area. And the first two kinds of data are integrated into five channels, which are red (R), green (G), blue (B), near infrared (NIR) and elevation channels. Second, we chose an area to make the labeled image with several classes contain surface natural resources. Third, we cut the image into training images and testing images, and the training images were made into 5000 images, 128 × 128 pixels to train the HR-Net model. Also for comparison, the experiment was carried out used the image without DEM data. Finally, we compared the accuracy, and our results showed that HR-Net model is useful and the image with DEM data has the better accuracy. Therefore, HR-Net and DEM data can be applied in practice to support the classification of surface natural resources.