Road segmentation of remote sensing images combined with dual attention mechanism
A new semantic segmentation model,ResNet-SPPCSPC-dual channel attention-UNet (RSD-UNet ),based on an improved U network structure,is proposed to solve the problem of false negative and misjudgement in road segmentation of optical remote sensing images.Firstly,the coding module adopts ResNet-34 with residual structure to avoid gradient disappearance of neural network.Secondly,serial modified SPPCSPC pooling module is integrated to improve the receptive field of the network and solve the multi-scale problem of road characteristics.Finally,the dual attention mechanism (DAM)of multi-spectral channels and spatial dimensions is integrated after the up-sampling operation.The experimental results show that on the CHN6-CUG dataset,compared with the benchmark network UNet,the index IoU and F1 scores increase by 4.4％ and 3.07％.Therefore,RSD-UNet can better achieve road segmentation for optical remote sensing images.
NETL
NSTL
万方数据
