| Summary: | The road information in high-resolution images tend to be disturbed by non-road information such as vegetation shadows, tall buildings and rivers.To address the problem, an improved model is proposed to extract road parts from high-resolution remote sensing images.For the construction of the model, a channel-spatial bi-attention mechanism is introduced to capture the global characteristic dependence of road information in the central region.Then the new hyperparameter weight loss is constructed based on the DICE+BCE loss of the original model to reduce the error of the parameter iteration in the network model and improve the accuracy of road segmentation.The hyperparameter weight ratio is successively set to 1:1, 2:1, 3:1, 4:1 and 5:1, and the best road segmentation performance of the model is obtained based on the adjustment of the hyperparameter weight ratio. The experimental results show that compared with FCN-8s, U-Net and other models, the improved D-LinkNet model delivers a significant improvement in road segmentation effect.The algorithm can effectively avoid false detection and missed detection that are caused by interference of non-road factors in road extraction.
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