基于EDRNet模型的高分辨率遥感影像道路提取

Road Extraction from High-Resolution Remote Sensing Images Based on EDRNet Model

针对高分辨率遥感影像道路提取结果不完整、边界质量差的问题,提出基于EDRNet模型的遥感影像道路提取方法。利用残差网络构建道路提取模型EDR1,保留道路的细节信息并加速网络收敛。通过融合多尺度、多层次的道路特征信息,设计道路提取结果优化模型EDR2。在此基础上,利用混合损失函数,提高道路提取的完整度。实验结果表明,EDRNet道路提取方法在马萨诸塞州道路数据集上的召回率、精确率和F1-score指标分别达到了84.4%、81.7%及83.0%,其结果完整且准确。

The existing methods for extracting the road parts from high-resolution remote sensing images are limited by the incomplete extraction results and poor boundary quality.To address the problem,a new method based on the EDRNet model is proposed for extracting road parts from remote sensing images.The residual network is used to build the road extraction model,EDR1,which retains the detailed information of the road and accelerates the network convergence.Then multi-scale and multi-level road feature information is fused to design a model,EDR2,for optimizing the road extraction results.On this basis,the mixed loss function is designed to make the road extraction results more complete.Experimental results on the Maschusetts Roads dataset show that the recall rate,precision and F1-score of the proposed methods reach 84.4%,81.7%,and 82.9%respectively.The proposed method can provide complete and accurate extraction results.