一种用于高分辨率遥感图像的建筑提取方法,具有多重注意力和并行编码器,结合了增强的光谱信息
Zhaojun Pang1, Rongming Hu1, Wu Zhu2
1School of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
这项研究介绍了MARS-Net,这是一个新的深度学习模型,用于从遥感图像中准确地从像素级别提取建筑物. 该模型通过集成并行编码器,注意力机制和光谱增强来改善建筑细分,从而实现了卓越的性能.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 地理信息系统是地理信息系统.
背景情况:
- 从高分辨率遥感图像中精确的像素级建筑提取对于地理空间应用至关重要.
- 不同地区建筑形状,分布和复杂的光谱特征的变化对一致的细分构成了挑战.
- 现有的方法很难保持稳定的细分效应,并有效地捕捉多个规模的建筑细节.
研究的目的:
- 开发一个先进的深度学习模型,从遥感数据中进行强大而准确的像素级建筑提取.
- 为了应对各种建筑外观和复杂的光谱特征在遥感图像中的挑战.
- 改善建筑物在不同地理环境中的细分性能和细节保存.
主要方法:
- 提出了一个并行编码的建筑提取网络 (MARS-Net),利用深层卷积神经网络 (DCNN) 和变压器进行本地和全球特征提取.
- 集成的坐标注意力 (CA) 和卷积块注意力模块 (CBAM) 增强编码器和解码器之间的空间和语义信息传输.
- 集成的密集的空心空间金字塔聚合 (DenseASPP) 用于多尺度的上下文信息捕获,以及用于多频段数据融合和增强的光谱信息增强模块 (SIEM).
主要成果:
- 与现有方法相比,MARS-Net显示出优越的建筑提取性能.
- 包括光谱信息增强模块 (SIEM) 进一步提高了细分的准确性和细节性.
- 在西安数据集上实现了87.53%的IOU,在WHU数据集上达到89.62%,F1分数分别为93.34%和94.52%.
结论:
- 马斯网在从高分辨率遥感图像中提取像素级建筑物方面取得了重大进展.
- 提出的注意力机制和光谱增强模块有效地解决了与建筑变性和光谱复杂性相关的挑战.
- 该模型的高IOU和F1分数验证了其在实际地理空间信息应用中的有效性.
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