Road extraction method based on improved DeepLabv3+remote sensing images combined with MobileNet
The existing deep learning network faces problems of low segmentation accuracy,high false detection rate,and low detection efficiency when extracting roads in remote sensing images.In order to address these issues,an improved model,namely MB-DeepLabv3+was proposed. At the encoder layer,MobileNetv3 was used as the feature extraction network,and a global attention mechanism was introduced to calculate the sample attention weight at the spatial and channel levels;at the decoder layer,a dense upsampling convolution kernel was used instead of the bilinear interpolation method to perform upsampling in feature maps. The experimental results on the Deep Globe dataset show that the accuracy of the proposed algorithm reaches 98.39％,which is 2.6％ higher than that of the original DeepLabv3+,and the extraction and calculation efficiency on a single image is improved. For road images with different levels of complexity,the proposed algorithm significantly improves the problems of loopholes and false extraction and can realize efficient and accurate road extraction compared with other models in the control group.
万方数据
维普
