用量子卷积增强注意力U-Net，提升突尼斯城区建筑分割效率
A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data
- 引入量子卷积预处理，提取雷达图像更丰富的结构特征
- 在保持精度的前提下，模型参数量显著减少
- 适合关注高效城市遥感分割的科研与应用人员
城市区域的建筑分割在城市规划、灾害响应和人口估算等领域具有重要意义。然而，高分辨率卫星图像在密集城区的建筑分割仍面临挑战。本研究针对突尼斯城市景观，利用哨兵-1合成孔径雷达（Sentinel-1 SAR）影像，探索使用量子卷积（Quanvolution）预处理来增强注意力U-Net模型的分割能力。通过量子卷积提取更具信息量的特征图，有效捕捉雷达影像中的关键结构细节，有助于提升建筑分割精度。初步结果显示，该方法在测试准确率上与标准注意力U-Net相当，但网络参数大幅减少。这一结果与前期研究一致，证实量子卷积不仅能保持模型精度，还能提高计算效率。这些成果表明，量子辅助深度学习框架在大规模城市建筑分割中具有应用潜力。
原文摘要 · Abstract (English)
Building segmentation in urban areas is essential in fields such as urban planning, disaster response, and population mapping. Yet accurately segmenting buildings in dense urban regions presents challenges due to the large size and high resolution of satellite images. This study investigates the use of a Quanvolutional pre-processing to enhance the capability of the Attention U-Net model in the building segmentation. Specifically, this paper focuses on the urban landscape of Tunis, utilizing Sentinel-1 Synthetic Aperture Radar (SAR) imagery. In this work, Quanvolution was used to extract more informative feature maps that capture essential structural details in radar imagery, proving beneficial for accurate building segmentation. Preliminary results indicate that proposed methodology achieves comparable test accuracy to the standard Attention U-Net model while significantly reducing network parameters. This result aligns with findings from previous works, confirming that Quanvolution not only maintains model accuracy but also increases computational efficiency. These promising outcomes highlight the potential of quantum-assisted Deep Learning frameworks for large-scale building segmentation in urban environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。