DEGANet: Road Extraction Using Dual-Branch Encoder With Gated Attention Mechanism

Automatic identification and extraction of roads from high-resolution remote sensing images (RSIs) are important in remote sensing and computer vision. Advancements in remote sensing technology have increased the information in images, making road extraction more challenging. Conventional convolutional methods have limitations, such as loss of spatial details and inadequate fusion of multiscale features. To address these challenges, the letter introduces a novel encoder-decoder architecture called dual-branch encoder with gated attention mechanism network (DEGANet), for extracting road networks in remote sensing image (RSI). First, we propose a multigated informative self-attention (MGSA) module that combines information from dual-branch encoders. By integrating the ResNet and the dynamic snake convolution (DSC) block, which conforms to road shapes, the module emphasizes slender structures similar to roads, thus enhancing the extraction of road features and focusing on capturing more road details. Second, we also introduce the cascade receptive field enhancement (CRFE) module, which optimizes both accuracy and computational complexity. This module combines various receptive field enhancement modules to improve capture long-range dependencies and spatial information perception. Comprehensive experiments conducted on various public remote sensing road datasets demonstrate that our network attains greater segmentation accuracy (intersection over union (IoU) and
$F1$
score) and connectivity [average path length similarity (APLS)], validating the effectiveness of our proposed method.