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Feature Aggregation Network for Building Extraction from High-Resolution Remote Sensing Images

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PRICAI 2023: Trends in Artificial Intelligence (PRICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14327))

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Abstract

The rapid advancement in high-resolution satellite remote sensing data acquisition, particularly those achieving sub-meter precision, has uncovered the potential for detailed extraction of surface architectural features. However, the diversity and complexity of surface distributions frequently lead to current methods focusing exclusively on localized information of surface features. This often results in significant intra-class variability in boundary recognition and between buildings. Therefore, the task of fine-grained extraction of surface features from high-resolution satellite imagery has emerged as a critical challenge in remote sensing image processing. In this work, we propose the Feature Aggregation Network (FANet), concentrating on extracting both global and local features, thereby enabling the refined extraction of landmark buildings from high-resolution satellite remote sensing imagery. The Pyramid Vision Transformer captures these global features, which are subsequently refined by the Feature Aggregation Module and merged into a cohesive representation by the Difference Elimination Module. In addition, to ensure a comprehensive feature map, we have incorporated the Receptive Field Block and Dual Attention Module, expanding the receptive field and intensifying attention across spatial and channel dimensions. Extensive experiments on multiple datasets have validated the outstanding capability of FANet in extracting features from high-resolution satellite images. This signifies a major breakthrough in the field of remote sensing image processing. We will release our code soon.

X. Zhou and X. Wei—Equal contribution

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Authors and Affiliations

  1. Institut Polytechnique de Paris, Rte de Saclay, 91120, Palaiseau, France

    Xuan Zhou & Xuefeng Wei

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  1. Xuan Zhou
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  2. Xuefeng Wei
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Correspondence to Xuan Zhou or Xuefeng Wei .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fenrong Liu

  2. SEEK Limited, Cremorne, NSW, Australia

    Arun Anand Sadanandan

  3. MIMOS Berhad, Kuala Lumpur, Malaysia

    Duc Nghia Pham

  4. Universitas Indonesia, Depok, Indonesia

    Petrus Mursanto

  5. Tabcorp Holdings Limited, Melbourne, VIC, Australia

    Dickson Lukose

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhou, X., Wei, X. (2024). Feature Aggregation Network for Building Extraction from High-Resolution Remote Sensing Images. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14327. Springer, Singapore. https://doi.org/10.1007/978-981-99-7025-4_9

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  • DOI: https://doi.org/10.1007/978-981-99-7025-4_9

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  • Print ISBN: 978-981-99-7024-7

  • Online ISBN: 978-981-99-7025-4

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