Road extraction from high-resolution remote sensing images based on HU-Net
In view of the complex background information of high-resolution remote sensing images and the many interference factors of road extraction,a road extraction method based on HU-Net for high-resolution re-mote sensing images is proposed.This method first uses ResNet34 pre-trained on the ImageNet dataset as the fea-ture encoder of the model to strengthen the feature extraction capability of the model and extract more road features.Then,a hybrid convolution unit is added to the middle part of the model to connect the codecs,which enhances the model's ability to extract spatial context information,retain more detailed features of the road.Finally,the decoder of the model is constructed using transposed convolutional and ordinary convolutional.The experimental results show that the IoU and F1-score of this method on the DeepGlobe road dataset reached 0.6750 and 0.8060,which can extract road from high-resolution remote sensing images more completely and accurately,and reduce the influ-ence of surrounding ground objects and tree shade on road extraction.
remote sensing application technologyroad extractiondeep learningresidual networkcontext information
万方数据
维普
