Context-content collaborative network for building extraction from high-resolution imagery

In practical applications, different application fields have various requirements regarding the precision and completeness of building extraction. Too low precision or completeness may limit the application and promotion of building extraction. Obtaining a good trade-off between the precision and com-pleteness of building extraction is still a challenging issue. To deal with this issue, this paper proposes a context-content collaborative network (C³Net) with an encoder-decoder structure. It consists of a context-content aware module (C²AM) and an edge residual refinement module (ER²M). In the C²AM, a context-aware block and a content-aware block complement each other and capture the localization information of buildings and long-range dependencies between the locations of each building, respectively. Thanks to the capability of the conventional filter, the ER²M can refine the features of decoder output by deploying a residual atrous spatial pyramid pooling with feature edges at the scale of the original image. To explicitly guide the function of the ER²M, we introduce a separated deep supervision strategy before and after the ER²M, which can consciously refine our C³Net towards the precision or completeness of building extraction to a certain extent, and improve the overall detection performance. Compared with several classical and state-of-the-art methods, extensive experiments on three open and challenging datasets demonstrate that the proposed C³Net not only acquires competitive performance but also achieves a better trade-off between precision and completeness of building extraction. The source code is released at https://github.com/TongfeiLiu/ C3Net-for-building-extraction.

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