MAFNet：基于多尺度空洞融合网络的遥感影像建筑物提取方法

doi:10.11947/j.AGCS.2025.20240439

测绘学报 ›› 2025, Vol. 54 ›› Issue (6): 1094-1106.doi: 10.11947/j.AGCS.2025.20240439

MAFNet：基于多尺度空洞融合网络的遥感影像建筑物提取方法

董子博¹(), 王竞雪²(), 卜丽静², 房琳³, 许峥辉¹

^1.辽宁工程技术大学测绘与地理科学学院，辽宁　阜新　123000
^2.湘潭大学自动化与电子信息学院，湖南　湘潭　411105
^3.海克斯康测绘与地理信息系统（青岛）有限公司，山东　青岛　266114

收稿日期:2024-10-28 修回日期:2025-05-08 出版日期:2025-07-14 发布日期:2025-07-14
通讯作者: 王竞雪 E-mail:472320795@stu.lntu.edu.cn;xiaoxue1861@163.com
作者简介:董子博（2001—），男，硕士生，主要研究方向为遥感影像信息提取。E-mail：472320795@stu.lntu.edu.cn
基金资助:
湖南省自然科学基金(2022JJ30561);辽宁省应用基础研究计划(2022JH2/101300273)

MAFNet: building extraction method from remote sensing images based on multi-scale atrous fusion network

Zibo DONG¹(), Jingxue WANG²(), Lijing BU², Lin FANG³, Zhenghui XU¹

^1.School of Geomatics, Liaoning Technical University, Fuxin 123000, China
^2.School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China
^3.Hexagon Geosystems (Qingdao) Co., Ltd., Qingdao 266114, China

Received:2024-10-28 Revised:2025-05-08 Online:2025-07-14 Published:2025-07-14
Contact: Jingxue WANG E-mail:472320795@stu.lntu.edu.cn;xiaoxue1861@163.com
About author:DONG Zibo (2001—), male, postgraduate, majors in remote sensing image information extraction. E-mail: 472320795@stu.lntu.edu.cn
Supported by:
Natural Science Foundation of Hunan Province(2022JJ30561);Fundamental Applied Research Foundation of Liaoning Province(2022JH2/101300273)

摘要/Abstract

摘要：

遥感影像建筑物提取对灾害管理、城市规划及变化监测等领域具有重要意义。由于城市建筑物大小不一，一张遥感影像中存在多种不同尺寸大小的建筑物，使得影像中建筑物提取精度不足。为提升影像中不同尺寸大小建筑物的提取精度，本文提出一种利用多尺度空洞融合网络的遥感影像建筑物提取方法。以U-Net网络为基础，首先，在编码器和解码器部分融合残差结构，使其在训练过程中更好地传播梯度；然后，在编码-解码器的桥接部分提出一个多尺度空洞融合模块，该模块利用多种空洞卷积捕捉全局上下文特征，并进一步通过通道和空间注意力机制来增强特征表达，有效提升了影像中不同尺寸建筑物的提取精度；最后，通过设计一个混合损失函数提升整体的边界提取效果。基于WHU building和Massachusetts building数据集进行试验，并将本文方法与当前主流的语义分割网络进行对比。试验结果表明，本文方法可以显著地提升影像建筑物提取精度，能够适应各种尺寸大小的建筑物提取，对于建筑物边界的提取更加完整和平滑。

关键词: 遥感影像, 建筑物提取, U-Net, 多尺度空洞融合, 混合损失函数

Abstract:

Building extraction from remote sensing images is of great significance to disaster management, urban planning, and change monitoring. Due to the different sizes of urban buildings, there are buildings of multiple spatial scales in a remote sensing image, which makes the accuracy of building extraction in the image insufficient. In order to improve the extraction accuracy of buildings of different scales in the image, this paper proposes a remote sensing image building extraction method using a multi-scale atrous fusion network. Based on the U-Net network, the residual structure is first fused in the encoder and decoder parts to better propagate the gradient during the training process. At the same time, a multi-scale atrous fusion (MAF) module is proposed in the bridge part of the encoder-decoder. This module uses multiple atrous convolutions to capture global context features, and further enhances feature expression through channel and spatial attention mechanisms, effectively improving the extraction accuracy of buildings of different scales in the image. Finally, a hybrid loss function is designed to improve the overall boundary extraction effect. This paper conducts experiments based on the WHU building and Massachusetts building datasets, and compares the proposed method with the current mainstream semantic segmentation network. Experimental results show that the proposed method can significantly improve the accuracy of building extraction in images, can adapt to the extraction of buildings of various sizes, and can extract building boundaries more completely and smoothly.

Key words: remote sensing images, building extraction, U-Net, multi-scale atrous fusion, hybrid loss function

中图分类号:

P237

董子博, 王竞雪, 卜丽静, 房琳, 许峥辉. MAFNet：基于多尺度空洞融合网络的遥感影像建筑物提取方法[J]. 测绘学报, 2025, 54(6): 1094-1106.

Zibo DONG, Jingxue WANG, Lijing BU, Lin FANG, Zhenghui XU. MAFNet: building extraction method from remote sensing images based on multi-scale atrous fusion network[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(6): 1094-1106.

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模型	IoU	Accuracy	Precision	Recall	F₁值
FCN-8s	79.71	95.43	88.68	89.76	88.60
U-Net	77.47	95.36	88.36	87.46	87.90
DeepLabV3+	86.07	96.48	92.28	90.05	91.15
SegNet	82.70	96.95	91.39	89.74	90.55
BuildFormer	85.96	97.03	89.51	89.45	89.47
HD-Net	84.59	96.41	89.03	88.89	88.96
SDSC-UNet	86.04	97.24	91.97	90.88	91.42
MAFNet	88.01	97.55	92.38	93.22	92.79

模型	IoU	Accuracy	Precision	Recall	F₁值
FCN-8s	71.82	93.96	81.44	80.94	81.18
U-Net	73.43	94.12	82.35	83.66	82.99
DeepLabV3+	76.67	94.74	85.87	86.67	86.26
SegNet	74.29	94.21	84.76	83.11	83.93
BuildFormer	77.31	95.01	87.26	86.32	86.79
HD-Net	76.44	94.49	85.20	85.66	85.43
SDSC-UNet	79.38	94.87	87.22	86.61	86.91
MAFNet	82.21	95.15	88.37	87.38	87.87

模型	训练过程处理速度/（batch/s）	训练总时长/h	测试过程处理速度/（batch/s）	测试总时长/min	IoU/（%）	F₁值/（%）
FCN-8s	3.0	30.1	6.2	3.2	79.71	88.60
U-Net	3.2	28.6	6.8	3.0	77.47	87.90
DeepLabV3+	2.2	39.6	5.0	4.0	86.07	91.15
SegNet	2.8	32.2	6.0	3.4	82.70	90.55
BuildFormer	2.5	36.3	5.6	3.6	85.96	89.47
HD-Net	2.7	32.4	5.8	3.5	84.59	88.96
SDSC-UNet	2.4	37.8	5.1	3.9	86.04	91.42
MAFNet	2.7	32.4	5.9	3.4	88.01	92.79

模型	IoU	Accuracy	Precision	Recall	F₁值
U-Net	77.47	95.36	88.36	87.46	87.90
Res_UNet	82.24	96.27	90.50	91.41	90.95
Res_CBAM_UNet	79.37	96.05	90.69	89.15	89.91
Res_ECA_UNet	85.09	97.15	92.47	91.34	92.07
Res_SE_UNet	83.41	95.71	91.40	90.51	90.94
MAFNet	88.01	97.55	92.38	93.22	92.79

损失函数	IoU	Accuracy	Precision	Recall	F₁值
L_B	86.58	97.05	92.14	89.67	91.03
L_D	85.41	95.84	91.84	90.76	91.29
L_L	86.18	96.27	92.01	93.26	92.63
0.8L_B+0.1L_D+0.1L_L	85.24	96.17	90.34	90.67	90.51
0.6L_B+0.1L_D+0.3L_L	86.96	97.34	90.91	93.31	92.09
0.6L_B+0.2L_D+0.2L_L	88.01	97.55	92.38	93.22	92.79
0.6L_B+0.3L_D+0.1L_L	85.94	96.21	90.66	92.29	91.47
0.4L_B+0.3L_D+0.3L_L	86.38	96.54	91.06	91.43	91.24
0.33L_B+0.33L_D+0.33L_L	85.99	96.42	91.13	90.95	91.03
0.2L_B+0.6L_D+0.2L_L	85.61	96.23	90.45	91.30	90.88
0.2L_B+0.4L_D+0.4L_L	85.72	96.67	90.87	91.56	91.21
0.2L_B+0.2L_D+0.6L_L	86.43	96.59	90.98	91.27	91.12

MAFNet：基于多尺度空洞融合网络的遥感影像建筑物提取方法

MAFNet: building extraction method from remote sensing images based on multi-scale atrous fusion network

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