High-Frequency Attention U-Net for Road Segmentation in High-Resolution Remote Sensing Imagery

This paper explores the application of a singular expanding path of Frequency Attention U-Net (FAUNet), specifically employing its frequency attention mechanism for road detection in remote sensing. Contrasting with the full dual-path architecture of the recently proposed FAUNet, this study cap-italizes on only the high-frequency attentive path, tailored for edge detection in road segmentation tasks. By focusing on this single path, the modified FAUNet is adept at highlighting the intricate details necessary for accurate road boundary identification in high resolution remote sensing images. Comparative evaluations are conducted against traditional models like U-Net, U-Net++, and a generic CNN under consistent experimental conditions, including identical datasets, loss functions, and training loops. The findings reveal that this adapted version of FAUNet outperforms the other models in key performance metrics such as recall, F1 score, and Intersection over Union (IoU)