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MDCGA-Net: Multiscale Direction Context-Aware Network With Global Attention for Building Extraction From Remote Sensing Images

IEEE journal of selected topics in applied earth observations and remote sensing, 2024, Vol.17, p.8461-8476 [Peer Reviewed Journal]

ISSN: 1939-1404 ;EISSN: 2151-1535 ;DOI: 10.1109/JSTARS.2024.3387969 ;CODEN: IJSTHZ

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  • Title:
    MDCGA-Net: Multiscale Direction Context-Aware Network With Global Attention for Building Extraction From Remote Sensing Images
  • Author: Niu, Penghui ; Gu, Junhua ; Zhang, Yajuan ; Zhang, Ping ; Cai, Taotao ; Xu, Wenjia ; Han, Jungong
  • Subjects: Building extraction ; Buildings ; Context modeling ; Data mining ; deep learning (DL) ; Feature extraction ; global attention ; Logic gates ; multiscale direction context-aware ; Remote sensing ; Transformers
  • Is Part Of: IEEE journal of selected topics in applied earth observations and remote sensing, 2024, Vol.17, p.8461-8476
  • Description: Building extraction from remote sensing images (RSIs) requires exploring multiscale boundary detailed information and extracting it completely, which is challenging but indispensable. However, existing solutions tend to augment feature information solely through multiscale fusion and apply attention mechanisms to focus on feature relationships within a single layer while ignoring the multiscale information, which affects segmentation results. Therefore, enhancing the capability of the network to adaptively capture multiscale information and capture the global relationship of features remains a pivotal challenge in overcoming the aforementioned hurdles. To address the preceding challenge, we propose a Multiscale Direction Context-aware network with Global Attention (MDCGA-Net), employing a classic encoder-decoder architecture enhanced with direction information and global attention flow. Specifically, in the encoder part, the multiscale layer is used to extract contextual information from the interlayer. In addition, the multiscale direction context-aware module is adopted to adaptively acquire multiscale information. In the decoder part, we propose a global attention gate module to capture discriminative features. Furthermore, we construct an operation of attention feature flow to obtain the global relationship among the different features with long-range dependencies, which guarantees the integrity of results. Finally, we have performed comprehensive experiments on three public datasets to showcase the efficacy and efficiency of MDCGA-Net in building extraction.
  • Publisher: IEEE
  • Language: English
  • Identifier: ISSN: 1939-1404
    EISSN: 2151-1535
    DOI: 10.1109/JSTARS.2024.3387969
    CODEN: IJSTHZ
  • Source: Directory of Open Access Journals
    IEEE Xplore Open Access Journals
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