DSWFNet: dual-branch fusion of spatial and wavelet features for road extraction from remote sensing images.

ABSTRACT: Road extraction from remote sensing imagery is essential for urban planning, traffic monitoring, and emergency response. However, existing methods often focus solely on spatial-domain features, limiting their ability to model complex topological structures like narrow or fragmented roads. To address this limitation, we propose a dual-branch framework-DSWFNet-that fuses spatial and frequency domain features for road extraction. The model introduces a frequency-domain branch constructed via Discrete Wavelet Transform (DWT) to complement the RGB-based spatial branch in modeling fine image details. To further enhance feature representations, we design two dedicated attention mechanisms: the Multi-Scale Coordinate Channel Attention (MSCCA) module for spatial features, and the Enhanced Frequency

SUBMITTER: Zhang K 

PROVIDER: S-EPMC12855965 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature