基于视觉注意力机制的面向对象的建筑提取
Xiaole Shen1, Chen Yu1, Lin Lin1
1College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China.
PeerJ. Computer science
|September 14, 2023
概括
本研究介绍了一种先进的方法,用于从高分辨率遥感图像 (RSI) 中自动提取建筑物. 这种新的方法将特征提取与先前知识相结合,实现城市绘图和分析的高精度.
科学领域:
- 地理信息学是一种地理信息学.
- 城市遥感城市遥感
- 计算机视觉 计算机视觉
背景情况:
- 建筑物是城市发展的关键指标,对各种城市研究至关重要.
- 从高分辨率遥感图像 (RSI) 中自动提取建筑物对于城市规划,扩散分析和损害评估至关重要.
研究的目的:
- 为高分辨率的RSI开发一种有效的建筑提取方法.
- 将自下而上的低级特征提取与自上而下的先前知识指导相结合.
主要方法:
- 在RSI中考虑建筑特征的特征空间转换方法.
- 一种面向对象的方法,用于高分辨率的RSI影子提取.
- 整合低层特征与先前知识,以增强建筑物检测.
主要成果:
- 在建筑提取方面,用户和生产商的准确度超过95%.
- 总体准确度超过97%,数量误差低于1%.
- 在所有评估指标上,与传统方法相比,表现优越.
结论:
- 提出的方法有效地从高分辨率的RSI中提取建筑物.
- 整合特征提取和先前知识显著提高了准确性.
- 该方法在各种城市遥感应用中被证明是有效的.
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