Abstract
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.
Highlights
With the rapid development of city construction, buildings have become one of the most changeable artificial target types in basic geographical data [1]
Information was applied to building extraction such as edge extraction, image segmentation, digital surface model (DSM) data, light detection and ranging (LiDAR) point clouds, and the spatial information and features of high spatial resolution (HSR) images [5]
D-LinkNet [28], convolution conditional random field (ConvCRF) [42], detail-preserving smoothing classifier based on conditional random fields (DPSCRF) [18] and fully connected conditional random field (FullCRF) [43] were selected for the comparative experiment of multiscale-aware and segmentation-prior conditional random fields (MSCRF)
Summary
With the rapid development of city construction, buildings have become one of the most changeable artificial target types in basic geographical data [1]. Li et al [16] proposed a feature pair conditional random field (FPCRF) framework, which uses convolutional neural networks (CNNs) as a feature extractor to achieve fine-grained building segmentation These methods generally use traditional neural networks combined with traditional CRF. For buildings of different scales, it is difficult to extract sufficient features from a single receptive field This framework introduces D-LinkNet (LinkNet with a pretrained encoder and dilated convolution) [17] to model the relationship between the observed image data and the label for the first time. The pairwise potential reflect the linear combination of the spatial relationship of adjacent pixels and the local class label cost term It can effectively maintain the detailed information inside the buildings.
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