Road Extraction Method from Remote Sensing Images with Feature Consistency Perception
Road extraction is an important topic in remote-sensing information extraction.However,for cases when buildings and trees obstruct roads,existing road extraction methods have a weak global consistency in sensing road features,resulting in fragmented road extraction results.A feature enhancement and consistency awareness network(FECP-Net)is proposed to address this issue.The network comprises an initial road extraction network(CRE-Net)and a feature enhancement and consistency awareness(FECP)module.In this network,CRE-Net extracts the initial road information and features.In contrast,the FECP module enhances the consistency of road features.It improves the completeness of road extraction results by connecting rough road information with road features of different scales.The proposed method was compared with other methods,namely,DGRN,U-Net,and D-LinkNet,on the CHT,Massachusetts,and DeepGlobal datasets.The results on the Massachusetts dataset showed that compared to other methods,the proposed method increased the intersection over union(IOU)by 0.45 percentage points,3.36 percentage points,and 9.48 percentage points,respectively,the F1 scores increased by 1.26 percentage points,2.76 percentage points,and 8.12 percentage points,respectively,and the recall rates increased by 4.60 percentage points,5.93 percentage points,and 12.46 percentage points,respectively.The proposed method can extract the information of more complete roads and improve road fragmentation and disconnection extraction results.
deep learningremote sensingfeature consistency perceptionfeature enhancementroad extraction
NETL
NSTL
万方数据
