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    15827188114

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    测绘遥感信息工程全国重点实验室

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    1976-03-21

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    武汉大学

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    测绘遥感信息工程全国重点实验室

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    2004-07-01

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    武汉大学星湖实验大楼713

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Paper Publications

Current position: Home > Scientific Research > Paper Publications

CANet: A Spatial Structure Constraint and Local Semantic Awareness Based Network for Weakly Supervised Building Extraction

  • Time:1905-07-17
  • Hits:

  • DOI number:

    10.1109/tgrs.2025.3537099

  • Affiliation of Author(s):

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China

  • Journal:

    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

  • Funded by:

    This work was supported in part by the National Key Research and Development Program of China under

  • Key Words:

    Cams,Buildings,Semantics,Remote sensing,Feature extraction,Data mining,Accuracy,Semantic segmentation,Conditional random fields,Visualization,Building extraction,remote sensing,self-supervised learning (SSL),weakly supervised deep learning

  • Abstract:

    Benefitting from the easy availability of image-level labels, weakly supervised semantic segmentation (WSSS) methods based on class activation maps (CAMs) have made significant progress in building extraction from remote sensing imagery. However, image-level labels lack precise spatial locations and boundary ranges of buildings, posing challenges in achieving comprehensive and structurally clear building extraction. Furthermore, due to the complex background interference and the diversity of building in high-resolution remote sensing imagery (HRRS), small and sparse buildings suffer from insuf

  • Co-author:

    Bowen,Cai, Jinyang,Wang, Yu,Wang, Dongyang,Hou

  • Indexed by:

    Journal paper

  • Correspondence Author:

    Zhenfeng,Shao

  • Document Type:

    Article

  • Volume:

    63

  • ISSN No.:

    0196-2892

  • Translation or Not:

    no

  • Date of Publication:

    1905-07-17

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