Feature Enhancement Attention for Road Extraction in High-Resolution Remote Sensing Image

Road extraction from images captured via remote sensing is a pivotal task across multiple domains, encompassing urban planning and intelligent transportation systems. In the realm of high-resolution remote sensing, traditional approaches to road extraction confront obstacles pertaining to reduced accuracy and resilience. This study introduces an innovative methodology for road extraction tailored to high-resolution remote sensing data. The devised algorithm integrates a feature enhancement attention module alongside parallel feature fusion. Specifically, the feature enhancement attention module is introduced to augment the network's capacity in discerning road-related information by analyzing feature maps produced at varying resolutions. Subsequently, during feature map extraction, the parallel feature fusion technique is employed to merge shallow and deep features sharing the same resolution, thus effectively leveraging the strengths of both to enhance the model's precision. Moreover, the network undertakes the computation of correlations among feature maps of differing resolutions as well as the entire feature map, thereby facilitating a holistic grasp of the global structure and semantic information embedded within the image. Experimental evaluations conducted on the CHN6-CUG and Massachusetts datasets substantiate that the proposed approach outperforms prevailing mainstream methods for road extraction in terms of both accuracy and processing speed.