Building Extraction Based on U-net and Conditional Random Fields

The automatic extraction of buildings from remote sensing images plays an important role in many areas such as urban planning, geographic information system, and military reconnaissance. This paper presents an automatic building extraction method using the full convolutional neural network and conditional random fields (CRF). Firstly, a deep fully convolutional neural network that fits the building extraction task is designed and trained based on the U-net network structure. Secondly, based on the preliminary results produced by the deep fully convolutional network, the method of conditional random fields is used to optimize the extraction results, which improves the extraction efficiency of large building profiles. The experimental results show that the proposed method can accurately realize the automatic extraction of buildings from remote sensing images, with the recall, precision, and F1-score respectively 75.8%, 85%, and 80.1%. This method has good performance and no artificial design features, so it can be effectively applied to extract building targets from remote sensing images.