CSA-Net: Complex Scenarios Adaptive Network for Building Extraction for Remote Sensing Images

Abstract

Building extraction is significant for the intelligent interpretation of high-resolution remote sensing images (HRSIs). However, in some complex scenarios where the features of the building and its adjacent ground objects are similar, the current segmentation model cannot distinguish them effectively. Therefore, we propose a complex scenarios adaptive network (CSA-Net) for building extraction. CSA-Net is comprised of the hierarchical-context feature extraction (HFE) module, the global-local feature interaction (GFI) module, and the multiscale-adaptive feature fusion (MFF) structure. The HFE obtains high-level semantic information at different levels and fuses it with low-level detailed information by skipping connections to enhance the reasoning and perception ability of building structure in complex scenes. Then, the GFI acquires global-local features of buildings and their surrounding environment via dense multiscale dilated convolution. The information can be shared through efficient interaction among features, and irrelevant backgrounds can be suppressed. Then, in the up-sampling process, the MFF alleviates the feature loss and enhances the robustness of the network by using feature fusion after layer-by-layer adaptive weight allocation. Experiments show that CSA-Net outperforms other comparable methods, with intersection over union values of 79.99%, 89.75%, and 73.59%, respectively, on the Google Arlinton, WHU, and Massachusetts building datasets. The visual comparison results demonstrate that our method can enhance the accuracy of building extraction in complex scenes. Meanwhile, the efficiency results indicate our approach strikes a balance between calculation parameters and time and achieves high levels of efficiency.

Keywords

• Attention
• building extraction
• deep learning
• global-local features
• remote sensing images