期刊
REMOTE SENSING
卷 12, 期 23, 页码 -出版社
MDPI
DOI: 10.3390/rs12233983
关键词
HSR imagery; building extraction; conditional random fields; D-LinkNet; segmentation prior
类别
资金
- National Natural Science Foundation of China [41901306]
- Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan China [G1323519214]
- Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University [18E03]
- Open Research Project of the Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University [2018(B)002]
Building extraction is a binary classification task that separates the building area from the background in remote sensing images. The conditional random field (CRF) is directly modelled by the maximum posterior probability, which can make full use of the spatial neighbourhood information of both labelled and observed images. CRF is widely used in building footprint extraction. However, edge oversmoothing still exists when CRF is directly used to extract buildings from high spatial resolution (HSR) remote sensing images. Based on a computer vision multi-scale semantic segmentation network (D-LinkNet), a novel building extraction framework is proposed, named multiscale-aware and segmentation-prior conditional random fields (MSCRF). To solve the problem of losing building details in the downsampling process, D-LinkNet connecting the encoder and decoder is correspondingly used to generate the unary potential. By integrating multi-scale building features in the central module, D-LinkNet can integrate multiscale contextual information without loss of resolution. For the pairwise potential, the segmentation prior is fused to alleviate the influence of spectral diversity between the building and the background area. Moreover, the local class label cost term is introduced. The clear boundaries of the buildings are obtained by using the larger-scale context information. The experimental results demonstrate that the proposed MSCRF framework is superior to the state-of-the-art methods and performs well for building extraction of complex scenes.
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