A method of building extraction from remote sensing images based on MBFF-Net
Aiming at the problems of missing detection,false detection and inaccurate edge in building extraction based on convolutional neural network,a Multi-Branch Feature Fusion building extraction network MBFF-Net is proposed.Firstly,CBAM attention mechanism is introduced into the cope and crop part of VGG16-UNet to strengthen the learning of building feature information;Then,a Multi-Branch Feature Fusion Module is designed to replace the convolution block in the decoder,fuse the feature information of different receptive fields,enrich the feature expression ability,and capture the local and cross-channel feature relationship;Finally,MBFF-Net is constructed by combining CBAM attention mechanism and Multi-Branch Feature Fusion Module,and verified on WHU Dataset and Inria Dataset.The results show that compared with U-Net,PSPNet,SegNet and VGG16-UNet,MBFF-Net is the best in the four indicators of IoU,Precision,Recall and mPA.The extracted buildings are more complete,and the phenomenon of false detection and missing detection is reduced.MBFF-Net shows a good performance in the task of building extraction,which verifies its feasibility in building extraction.
building extractionattention mechanismmulti-branch feature fusionMBFF-Net
NETL
NSTL
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