人力资源-马巴:建筑足迹细分与几何驱动的边界规范化
Buyu Su1, Defei Yin2, Piyuan Yi1
1National Key Laboratory of Uranium Resource Exploration-Mining and Nuclear Remote Sensing, Beijing Research Institute of Uranium Geology, Beijing 100029, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
高分辨率-Mamba (HR-Mamba) 通过将高分辨率网络 (HRNet) 与高级建模相结合,改善了密集的城市地区的建筑提取. 这种方法提高了准确性,并产生了更光滑,工程准备的建筑轮.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 地理空间分析是什么
背景情况:
- 建筑提取对于城市规划和土地利用评估至关重要.
- 密集的城市场景带来了诸如错过的物体,粘附和当前方法中的不规则轮等挑战.
研究的目的:
- 开发一个高分辨率的语义细分网络,以改善建筑物提取.
- 解决密集的城市环境中的局限性,重点关注细节保真性和轮规律性.
主要方法:
- 引入了高分辨率-Mamba (HR-Mamba),通过边缘意识和序列状态建模来增强HRNet.
- 采用了Canny增强的干,基于卷积的块和基于Mamba的状态空间模型 (Mamba-SSM) 来实现边界稳定和远程依赖性捕获.
- 采用复合损失函数 (BCE, Dice, Boundary loss) 和基于特征的自适应后处理管道,用于精细的建筑轮.
主要成果:
- 在密集的城市图像上,HR-Mamba获得了83.93%的F1得分,比HRNet提高了2.98%.
- 该网络在建筑提取方面展示了增强的细节忠实性和全球一致性.
- 后处理管道成功生成了规律,光滑和工程准备的建筑轮.
结论:
- 在具有挑战性的城市景观中,HR-Mamba提供了一个强大的解决方案,用于高分辨率的建筑提取.
- 提出的方法有效地平衡了细节的保存和全球背景的理解.
- HR-Mamba为需要精确的建筑足迹划分的遥感应用提供了可通用的方法.
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