Building Extraction Network based on High-resolution Remote Sensing Image
The segmentation of buildings from the background in high-resolution remote sensing images faces several challenges, including difficulties in extracting multi-scale information, insufficient capture of long-range contextual information, and the underutilization of multi-scale features. Existing met...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/905/2025/isprs-archives-XLVIII-G-2025-905-2025.pdf |
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| Summary: | The segmentation of buildings from the background in high-resolution remote sensing images faces several challenges, including difficulties in extracting multi-scale information, insufficient capture of long-range contextual information, and the underutilization of multi-scale features. Existing methods often struggle to effectively capture features at different scales, which limits the segmentation accuracy. Furthermore, long-range contextual information is frequently overlooked, hindering model’s ability in understanding the global structure of buildings. Additionally, balancing low-level details with high-level semantic information poses challenges in effectively fusing multi-scale features from high-resolution imagery. To address these issues, this paper proposes the Multi-Scale Multi-Kernel Building Extraction Network (MMAENet), which significantly enhances the capability to capture multi-scale features through the integration of Poly Kernel Inception Network (PKINet), and improves the capture of long-range contextual information. The Panoramic Feature Pyramid (PFP) structure is introduced to ensure the full integration of both high-level and low-level information. Performance evaluation on the WHU Aerial dataset demonstrates that the model achieves superior accuracy in building segmentation compared to Convnext, PSPNet, and Swin Transformer. |
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| ISSN: | 1682-1750 2194-9034 |