关键词: 光学遥感影像, 多维特征, U-Net模型, 深度学习, 露天工程活动

Abstract: In order to effectively monitor the engineering activities with filling, digging, mining and stripping behaviors, this paper proposes a remote sensing multi-feature semantic segmentation model for pixel-level surface engineering activity extraction. In this model, GF-2 optical remote sensing images are used as the data source, and U-Net is used as deep neural network. By manual labeling, the surface engineering activity image samples are constructed for model training with multi-dimensional features, so as to achieve the effect of surface engineering activity extraction. The experimental result shows that the overall extraction accuracy of this method is 87.36%, and the average accuracy is 86.78%, which is better than KNN and SVM. The proposed method provides a technical reference for the automatic supervision of engineering activities.

Key words: optical remote sensing image, multi-feature, U-Net model, deep learning, surface engineering activity area