Depth Feature Fusion Network for Building Extraction in Remote Sensing Images

Abstract

Building extraction is a challenging task in remote sensing images (RSI) interpretation. Fusing RSI from different sources, such as high-resolution RSI and LiDAR, is a common strategy to improve the building extraction accuracy. However, the acquisition of multisource registered RSI of the same region is time-consuming and laborious, which limits the practical application of multisource fusion strategy. Benefiting from the success of large models in different fields in recent years, we proposed to extract depth information from large models and fuse it with corresponding RSI to improve the building extraction accuracy. Directly introducing the extracted depth information into the original image is not helpful for improving the building extraction accuracy. Therefore, we designed a dedicated deep feature fusion module (DFFM) for deep feature fusion. Specifically, we designed a depth feature fusion network (DFF-Net) composed of a depth information extraction module, a DFFM and a U-Net. The DFF-Net successfully improves building extraction accuracy by fusing the extracted depth information. To evaluate the validity of this method, we conducted abundant building extraction experiments on the WHU, Massachusetts, and Inria building datasets. Compared with comparative methods, our method obtained the highest IoU on all these three RSI building datasets, which demonstrates its simplicity and effectiveness.

Keywords

• Building extraction
• deep learning
• depth feature fusion
• depth information extraction
• remote sensing