Joint Spectral Information and Spatial Details for Road Extraction From Optical Remote-Sensing Images

Currently, satellite remote-sensing image acquisition systems typically include two forms of panchromatic and multispectral images, both of which have complementary advantages in spatial and channel dimensions. However, translating advantageous information into a deciphering function in road-extraction tasks remains a challenge. This study, therefore, proposes a road-extraction method combining spectral information and spatial details. First, a multibranch network framework was built based on an encoding–decoding structure. The encoding layers of the panchromatic and multispectral image branches were constructed from the residual modules. Fusion branches were then constructed during the decoding phase. The spectral information of the multispectral image and spatial details of the panchromatic image were then obtained using the HIS color transform and Haar wavelet transform, respectively, and injected into the fusion branch. A polarized self-attention mechanism was finally introduced at different levels of the fusion branch to reduce information loss during feature extraction, and operations, such as connected convolution and nonlinear activation, were later connected to complete the road prediction. The implementation of the proposed method on the GF2-FC and CHN6-CUG datasets revealed a superior performance compared with comparative methods in terms of quantitative evaluation metrics. The proposed method performed the strongest in several scenarios, particularly in difficult road-extraction areas, such as shadows and vegetation cover.