High-resolution building extraction based on the edge-aware network CEEAU_Net

Abstract

Spatial information such as building location and distribution plays an important role in urban dynamic monitoring and urban planning applications. In recent years, deep learning methods have developed rapidly and achieved state-of-the-art performance in building extraction from remote sensing images in a variety of scenarios. However, existing semantic segmentation models pay more attention to global semantic information, emphasize multi-scale feature fusion or set lighter acceptance domains to obtain more global features, and ignore low-level detail features such as edges. Therefore, a new end-to-end deep learning network CEEAU_Net based on encoder-decoder architecture is designed to add edge sensing module and edge feature extraction module to obtain edge feature information of buildings. The Luxian county area of Luzhou City, Sichuan province is selected for building dataset production, which is located in the Longmenshan seismic zone, with many earthquakes of magnitude three or above, and the scene is complex, so a more accurate building extraction method is needed. Comparison experiments are also conducted with several advanced models on two public datasets, WHU building dataset (WHU) and Massachusetts. Selection of multiple indicators for indicator evaluation of results. CEEAU_Net achieves the best results in the metrics of overall accuracy, F1-score, Intersection over Union (IoU) and Mean Intersection over Union (MIoU), which suggests that the method proposed in this paper can effectively improve the accuracy of building extraction.