Building Segmentation Based on Deep Feature Extraction and Multi Output Enhancement Fusion
This paper proposes a deep feature extraction and multi output enhanced building semantic segmentation network to address the problem of missing individual building parts and similar texture non building misclassification in high-resolution remote sensing image building semantic segmentation.Firstly,a parallel continuous void spatial attention pyramid module is designed at the alternation of encoding and decoding to achieve deep extraction of high-dimensional features of buildings.Then,in the network decoding stage,a multi-output enhanced fusion module is designed to improve the effective participation of building features at different scales in the output results.Selecting the similar algorithms of U-Net,DeeplabV3+,MA-FCN and BRRNet for comparison and testing on the public datasets of Massachusetts and WHU,the OA,precision,recall,F1 and IoU indicators can reach 98.87％,94.53％,95.40％,90.41％,and 94.96％,respectively.OA,F1,recall and IoU accuracy are higher than that of the other four indicators.The OA,precision,recall,IoU and F1 of the proposed method are higher than that of U-Net by 0.25％,1.12％,1.14％,2.02％,and 1.12％,respectively.
building extractionattention mechanismmulti output fusiondeep featureAtrous convolution
万方数据
维普
