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一个基于重建的变压器的双分支融合网络,用于在遥感图像中进行建筑提取
Yitong Wang1, Shumin Wang1, Aixia Dou1
1Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
一个新的双分支融合重建变压器网络 (DFRTNet) 改进了自动从高分辨率遥感图像中提取建筑物. 这种方法增强了全球和本地特征提取,在城市绘图中获得更高的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 自动建筑提取对于城市规划,人口统计和灾害评估至关重要.
- 卷积神经网络 (CNN) 和变压器对于语义细分很常见,但在特征提取方面存在局限性.
- 由于固定的结构,CNN与全球特征作斗争,而变压器在计算上可能是多余的,并且缺乏本地细节.
研究的目的:
- 提出一个新的双分支融合重建变压器网络 (DFRTNet),以从高分辨率遥感图像 (HRSI) 中高效准确地提取建筑物.
- 解决现有的CNN和变压器模型在捕捉全球背景和本地细节方面的局限性.
主要方法:
- 开发了一个具有双分支编码器的DFRTNet,用于本地和全球特征提取.
- 集成了本地和全球特征提取 (LGFE) 模块,为全球特征提供动态范围注意力 (DRA),为细粒度细节提供本地特征提取 (LFE) 分支.
- 使用多层感知器 (MLP) 进行特征融合,并在解码器中使用通道注意模块 (CAM) 进行增强的特征表示.
主要成果:
- 与现有的最先进的方法相比,DFRTNet在建筑细分方面表现优越.
- 在WHU和马萨诸塞州建筑数据集上实现了最佳的细分精度.
- 拟议的网络有效地平衡了全球上下文建模和本地特征提取.
结论:
- DFRTNet提供了一种高效准确的解决方案,用于从HRSI中自动提取建筑物.
- 采用DRA和LFE的双分支架构有效地捕捉了多个尺度的特征.
- 这项研究推进了用于遥感应用的语义细分技术.
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