Lightweight road extraction model based on multi-scale feature fusion
A road extraction model based on multi-scale feature fusion lightweight DeepLab V3+(MFL-DeepLab V3+)was proposed aiming at the problems of high computational complexity and poor road extraction effect of the current semantic models used in the field of remote sensing image road extraction.The lightweight MobileNet V2 network was used to replace the original model's Xception network as the backbone network in order to reduce the parameters of the model and the computational complexity of the model.Deep separable convolution was introduced into the Atlas spatial pyramid pooling(ASPP)module.A multi-scale feature fusion with attention(MFFA)was proposed in the decoding area in order to enhance the road extraction ability of the model and optimize the extraction effect on small road segments.Experiments based on the Massachusetts roads dataset showed that the parameter size of the MFL-DeepLab V3+ model was significantly reduced with a parameter compression of 88.67%compared to the original model.The road extraction image had clear edges,and its accuracy,recall,and F1-score were 88.45%,86.41%and 87.42%,achieving better extraction performance compared to other models.
NETL
NSTL
万方数据
维普
