Automatic Building Extraction from Remote Sensing Images Based on Cascaded CRFs and the U-Net Deep Learning Model

To address the problem of building edge ambiguity in deep learning models for building extraction, cascaded CRFs (fully connected conditional random fields) is introduced into the U-Net model and an improved U-Net model (U-Net+cascade CRFs) is proposed for automatic building extraction from remote sensing images. A cascaded CRFs model is constructed and introduced into the decoding layer of the U-Net model to learn the boundary information from the multi-layer structure and enhance the ability of the model to segment the building boundary. Taking Foshan City, Guangdong Province, as the research area, the U-Net+cascaded CRFs, U-Net+CRFs, U-Net and SVM models are used to carry out building extraction experiments. The results show that the proposed method can effectively identify building boundary information and improve the accuracy of building extraction: it can achieve 93.1%, 87.5%, 91.4% and 85.1% of the four indexes of accuracy, recall rate, F1 value and cross/combine ratio respectively, which are superior to those of U-Net+CRFs, U-Net and SVM models.