MDCGA-Net: Multiscale Direction Context-Aware Network With Global Attention for Building Extraction From Remote Sensing Images

Abstract

Building extraction from remote sensing images (RSIs) requires exploring multiscale boundary detailed information and extracting it completely, which is challenging but indispensable. However, existing solutions tend to augment feature information solely through multiscale fusion and apply attention mechanisms to focus on feature relationships within a single layer while ignoring the multiscale information, which affects segmentation results. Therefore, enhancing the capability of the network to adaptively capture multiscale information and capture the global relationship of features remains a pivotal challenge in overcoming the aforementioned hurdles. To address the preceding challenge, we propose a Multiscale Direction Context-aware network with Global Attention (MDCGA-Net), employing a classic encoder–decoder architecture enhanced with direction information and global attention flow. Specifically, in the encoder part, the multiscale layer is used to extract contextual information from the interlayer. In addition, the multiscale direction context-aware module is adopted to adaptively acquire multiscale information. In the decoder part, we propose a global attention gate module to capture discriminative features. Furthermore, we construct an operation of attention feature flow to obtain the global relationship among the different features with long-range dependencies, which guarantees the integrity of results. Finally, we have performed comprehensive experiments on three public datasets to showcase the efficacy and efficiency of MDCGA-Net in building extraction.

Keywords

• Building extraction
• deep learning (DL)
• global attention
• multiscale direction context-aware