MDCGA-Net: Multi-Scale Direction Context-Aware Network with Global Attention for Building Extraction from Remote Sensing Images

Building extraction from remote sensing images (RSIs) requires exploring multi-scale boundary detailed information and extracting it completely, which is challenging but indispensable. However, existing solutions tend to augment feature information solely through multi-scale fusion and apply attention mechanisms to focus on feature relationships within a single layer while ignoring the multi-scale information, which affects segmentation results. Therefore, enhancing the capability of the network to adaptively capture multi-scale 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 Multi-scale 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 multi-scale layer (MSL) is used to extract contextual information from the inter-layer. Additionally, the multi-scale direction context-aware module (MDCM) is adopted to adaptively acquire multi-scale information. In the decoder part, we propose a global attention gate module (GAGM) 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.