Building extraction method based on MFF-Deeplab v3+network for high-resolution remote sensing images
Automatic extraction of building information from high-resolution remote sensing images is of great significance in the fields of environmental monitoring,earthquake mitigation,and land use,making it a research hotspot in the field of high-resolution remote sensing applications.In order to improve the accuracy of building extraction from high-resolution remote sensing images,a building extraction method based on MFF-Deeplabv3+(multiscale feature fusion-Deeplabv3+)network for high-resolution remote sensing images is proposed in this paper.First,the multi-scale feature enhancement module is designed to enable the network to capture more scale context information;then,the feature fusion module is designed to effectively fuse deep features with shallow features to reduce the loss of detail information;finally,the attention mechanism module is introduced to select accurate features adaptively.In the comparison experiments of the Inria building dataset,MFF-Deeplabv3+achieved the highest accuracy in PA,MPA,FWIoU,and MIoU metrics with 95.75％,91.22％,92.12％,and 85.01％,respectively,while the generalization experiments of the WHU building dataset achieved good results.The results show that this method extracts building information from high-resolution remote sensing images with high accuracy and strong generalization.
building extractiondeep learningattention mechanismmulti-scale feature enhancementhigh-resolution remote sensing images
万方数据
维普
