期刊
REMOTE SENSING
卷 12, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/rs12091400
关键词
building extraction; attention block; multiple losses; semantic segmentation; remote sensing images
类别
资金
- National Natural Science Foundation of China [41701446, 41971356]
Semantic segmentation of high-resolution remote sensing images plays an important role in applications for building extraction. However, the current algorithms have some semantic information extraction limitations, and these can lead to poor segmentation results. To extract buildings with high accuracy, we propose a multiloss neural network based on attention. The designed network, based on U-Net, can improve the sensitivity of the model by the attention block and suppress the background influence of irrelevant feature areas. To improve the ability of the model, a multiloss approach is proposed during training the network. The experimental results show that the proposed model offers great improvement over other state-of-the-art methods. For the public Inria Aerial Image Labeling dataset, the F1 score reached 76.96% and showed good performance on the Aerial Imagery for Roof Segmentation dataset.
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