基于脚点热度图和双向连接图的遥感影像建筑物提取方法

张利利; 张津铭; 刘雄飞; 乔海浪; 王宏琦

doi:10.11999/JEIT220201

基于脚点热度图和双向连接图的遥感影像建筑物提取方法

doi: 10.11999/JEIT220201

张利利^{1, 2, 3, 4},
张津铭^{1, 4, ,},
刘雄飞^{1, 4},
乔海浪^{1, 4},
王宏琦^{1, 2, 3, 4}

1.
中国科学院空天信息创新研究院北京 100190
2.
中国科学院大学北京 100049
3.
中国科学院大学电子电气与通信工程学院北京 100049
4.
中国科学院网络信息体系技术重点实验室北京 100190

基金项目: 中国科学院青年创新促进会基金(E03307020D)，创新研究专项基金(2022C61540)

详细信息

作者简介:
张利利：女，副研究员，研究方向为遥感应用技术

张津铭：男，助理研究员，研究方向为遥感数据处理

刘雄飞：男，副研究员，研究方向为地图学与地理信息系统

乔海浪：男，助理研究员，研究方向为遥感应用技术

王宏琦：男，研究员，博士生导师，研究方向为遥感图像处理以及目标识别等

通讯作者:
张津铭　nicnyzjm@whu.edu.cn

中图分类号: TN911.73; P237
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- PDF下载量: 85
- 被引次数: 0
出版历程
- 收稿日期: 2022-03-01
- 修回日期: 2022-06-02
- 录用日期: 2022-06-22
- 网络出版日期: 2022-06-28
- 刊出日期: 2023-04-10

Building Extraction from Satellite Imagery Based on Footprint Map and Bidirectional Connection Map

ZHANG Lili^{1, 2, 3, 4},
ZHANG Jinming^{1, 4
, ,},
LIU Xiongfei^{1, 4},
QIAO Hailang^{1, 4},
WANG Hongqi^{1, 2, 3, 4}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
4.
Key Laboratory of Network Information System Technology, Chinese Academy of Sciences, Beijing 100190, China

Funds: The Youth Innovation Promotion Association Foundation (E03307020D), The Special Funds for Creative Research (2022C61540)

摘要

摘要: 目前，大多数基于深度学习的遥感影像建筑物提取方法采用语义分割的方式，对遥感影像进行二分类预测。然而，该类方法没有考虑建筑物的几何特性，难以进行精确提取。为了更精确地提取建筑物，该文引入建筑物的几何信息，提出一种基于脚点热度图和双向连接图的建筑物轮廓提取方法。该方法为一种多分支的深度卷积网络，分别对建筑物的脚点以及脚点间的连接性进行预测。在其中一个分支中，预测建筑物的脚点热度图，并用非极大抑制算法得到建筑物的脚点像素坐标；利用另外两个分支预测脚点之间的正向连通性和反向连通性，并通过这种双向连接图对脚点间是否具有连接性进行判断，在将具有连通性的脚点进行连接后，可得到最终的建筑物轮廓。该文算法在Buildings2Vec数据集上进行了验证，结果表明该方法在遥感影像建筑物提取中具有一定的优越性。
- 遥感影像 /
- 深度学习 /
- 脚点预测 /
- 双向连接图
Abstract: Most state-of-the-art building extraction from satellite imagery are based on binary segmentation. However, the geographic information has not been considered in these methods, thus, it is difficult to extract building accurately. To consider fully the geographic information on feature extraction, a building extraction convolutional neural network based on footprint map and bidirectional connection is proposed. The proposed method is a multi-branch network, which is designed to predict the footprint and bidirectional connection map, respectively. This paper predicts the footprint heatmap of buildings and uses the Non-Maximum Suppression (NMS) algorithm to obtain the pixel coordinates. Another two branches are used to predict positive connectivity and negative connectivity between footprints. Each pair of nodes is connected according to the bidirectional connectivity map to obtain the final building outline. Experiments on the Buildings2Vec dataset demonstrate that the proposed method outperforms various previous work, which illustrate the superiority in building extraction from satellite imagery.
- Satellite imagery /
- Deep learning /
- Footprint prediction /
- Bidirectional connection map

HTML全文

图 1 基于脚点热度图和双向连接图的遥感影像建筑物提取网络

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图 2 建筑物脚点热度图效果图

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图 3 建筑物脚点提取效果图

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图 4 建筑物脚点双向连接图

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图 5 Buildings2Vec数据集示意图

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图 6 双向连接性与单向连接性效果对比图

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图 7 不同算法在训练数据集上的Loss曲线

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图 8 双向连接性与单向连接性效果对比图

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图 9 WHU Building Dataset建筑物轮廓提取结果

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表 1 双向连接性效果定量指标

方法	${P}_{{\rm{c}}}$	$ {R}_{{\rm{c}}} $	$ {{\rm{F}}1}_{{\rm{c}}} $	$ {P}_{{\rm{e}}} $	$ {R}_{{\rm{e}}} $	$ {{\rm{F}}1}_{{\rm{e}}} $
单向连接性效果	0.790	0.832	0.810	0.575	0.661	0.615
双向连接性效果	0.803	0.846	0.824	0.591	0.682	0.633

下载: 导出CSV

表 2 本文方法与其他方法定量化对比

方法	$ {P}_{{\rm{c}}} $	$ {R}_{{\rm{c}}} $	$ {{\rm{F}}1}_{{\rm{c}}} $	$ {P}_{{\rm{e}}} $	$ {R}_{{\rm{e}}} $	$ {{\rm{F}}1}_{{\rm{e}}} $
PPGNet	0.893	0.694	0.781	0.737	0.501	0.596
Conv-MPN	0.779	0.802	0.790	0.569	0.607	0.587
本文方法	0.803	0.846	0.824	0.591	0.682	0.633

下载: 导出CSV

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