Raster Map Line Element Extraction Method Based on Improved U-Net Network

Abstract

To address the problem of low accuracy in line element recognition of raster maps due to text and background interference, we propose a raster map line element recognition method based on an improved U-Net network model, combining the semantic segmentation algorithm of deep learning, the attention gates (AG) module, and the atrous spatial pyramid pooling (ASPP) module. In the proposed network model, the encoder extracts image features, the decoder restores the extracted features, the features of different scales are extracted in the dilated convolution module between the encoder and the decoder, and the attention mechanism module increases the weight of line elements. The comparison experiment was carried out through the constructed line element recognition dataset. The experimental results show that the improved U-Net network accuracy rate is 93.08%, the recall rate is 92.29%, the DSC accuracy is 93.03%, and the F1-score is 92.68%. In the network robustness test, under different signal-to-noise ratios (SNRs), comparing the improved network structure with the original network structure, the DSC improved by 13.18–17.05%. These results show that the network model proposed in this paper can effectively extract raster map line elements.