High-Precision Segmentation of Buildings with Small Sample Sizes Based on Transfer Learning and Multi-Scale Fusion

Abstract

In order to improve the accuracy of the segmentation of buildings with small sample sizes, this paper proposes a building-segmentation network, ResFAUnet, with transfer learning and multi-scale feature fusion. The network is based on AttentionUnet. The backbone of the encoder is replaced by the ResNeXt101 network for feature extraction, and the attention mechanism of the skip connection is preserved to fuse the shallow features of the encoding part and the deep features of the decoding part. In the decoder, the feature-pyramid structure is used to fuse the feature maps of different scales. More features can be extracted from limited image samples. The proposed network is compared with current classical semantic segmentation networks, Unet, SuUnet, FCN, and SegNet. The experimental results show that in the dataset selected in this paper, the precision indicators of ResFAUnet are improved by 4.77%, 2.3%, 2.11%, and 1.57%, respectively, compared with the four comparison networks.