基于道路拓扑关联特征的城乡道路面精细提取网络

doi:10.11947/j.AGCS.2025.20240124

摘要/Abstract

摘要：

深度学习方法已成为基于遥感影像数据的城乡道路网分类提取的主流技术。然而，现有方法存在邻近地物（如植被和建筑物等）混杂遮挡、模型训练时间长、计算复杂度高等问题，并且大多仅关注道路面、边缘线和中心线等独立目标，导致道路分类提取结果精度不高。为了充分利用道路边缘与道路面之间的空间拓扑关系约束特征，本文提出了一种基于道路拓扑关联特征信息的道路面提取网络，记作CAS-DeepNet。首先，基于DeepLabV3+网络架构，改进轻量级MobileNetV2特征提取网络，嵌入基于残差连接的边缘增强模块以捕获道路边缘信息；其次，设计基于密集连接的CS-ASPP结构以提高模型性能；然后，引入通道注意力机制，有效地融合图像中的多分支通道，以提高特征表征能力；最后，通过道路边缘拓扑关联信息构建道路连通性约束，以提升道路网提取结果完整性。在CHN6-CUG和DeepGlobe等数据集进行试验，结果表明，本文设计的CAS-DeepNet与当前流行的U-Net++、DeepLabV3+、D-LinkNet、RoadNet、ACNet和SDUNet等方法相比，在准确率、召回率、F₁值和总体精度等评价指标方面更具优势，能够明显提升道路路网提取结果精度与完整性。本文方法可为自然资源调查监测和地理空间环境感知建模提供基础支撑。

关键词: 道路提取, 边缘增强模块, 改进DeepLabV3+, CS-ASPP, 注意力机制

Abstract:

Deep learning methods have become the mainstream technology for classifying and extracting urban and rural road networks based on remote sensing image data. However, the existing methods suffer from issues such as mixed occlusion of neighboring objects (such as vegetation and buildings), long model training time, and high computational complexity, and most of them only focus on independent targets such as road surface, edge line, and center line, resulting in low accuracy of road classification extraction results. In order to fully utilize the constrains of the spatial topological relationship features between road edges and road surfaces, this paper proposes a road surface extraction network, CAS-DeepNet, based on the road topological correlation feature information. Firstly, based on the DeepLabV3+network architecture, the lightweight MobileNetV2 feature extraction network is improved, and the edge enhancement module based on residual connection is embedded to capture road edge information. Secondly, a CS-ASPP structure based on dense connections is designed to improve the model performance. Then, the channel attention mechanism is introduced to effectively fuse multiple branch channels in the image to improve the feature representation ability. Finally, the road connectivity constraints are constructed through the topological association information of road edges to enhance the integrity of the road network extraction results. The experimental results on datasets, such as CHN6-CUG and DeepGlobe, show that the CAS-DeepNet designed in this paper has more advantages over popular methods, such as U-Net++, DeepLabV3+, D-LinkNet, RoadNet, ACNet, and SDUNet, in terms of accuracy rate, recall rate, F₁ score, and overall accuracy rate, significantly improving the accuracy and completeness of the extraction results of road network. This road surface fine extraction method based on road topology correlation features proposed in this study can provide basic support for natural resource survey and monitoring, as well as geospatial environment perception modeling.

Key words: road extraction, edge enhancement module, improved DeepLabV3+, CS-ASPP, attention mechanism

中图分类号:

P237

王艳军, 唐徐超, 王成, 蔡恒藩. 基于道路拓扑关联特征的城乡道路面精细提取网络[J]. 测绘学报, 2025, 54(1): 75-89.

Yanjun WANG, Xuchao TANG, Cheng WANG, Hengfan CAI. Urban and rural road surface extraction network based on road topological correlation features[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(1): 75-89.

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Input	Operator	t	c	n	s
512×512×3	Conv2d	—	32	1	2
256×256×32	Bottleneck	1	16	1	1
256×256×16	Bottleneck	6	24	2	2
128×128×24	Bottleneck	6	32	3	2
64×64×32	Bottleneck	6	64	4	2
32×32×64	Bottleneck	6	96	3	1
32×32×96	Bottleneck	6	160	3	1
32×32×160	Bottleneck	6	320	1	1

数据集	模型	Pre	Rec	F₁	OA	IoU
	U-Net++	0.706 7	0.746 2	0.725 9	0.952 7	0.569 8
	D-LinkNet	0.747 5	0.837 2	0.789 8	0.969 1	0.652 6
	RoadNet	0.760 5	0.795 1	0.777 4	0.967 8	0.635 9
CHN6-CUG	DeepLabV3+	0.738 8	0.810 0	0.772 8	0.960 5	0.629 7
	ACNet	0.786 7	0.843 4	0.814 1	0.963 6	0.684 1
	SDUNet	0.812 6	0.757 2	0.783 9	0.970 4	0.692 7
	CAS-DeepNet	0.781 2	0.860 5	0.818 9	0.974 4	0.693 4
	U-Net++	0.657 5	0.723 6	0.688 9	0.972 3	0.525 5
	D-LinkNet	0.669 1	0.759 3	0.711 3	0.971 8	0.552 0
	RoadNet	0.661 3	0.745 6	0.700 9	0.972 0	0.539 5
DeepGlobe	DeepLabV3+	0.726 9	0.738 3	0.732 5	0.967 0	0.578 0
	ACNet	0.743 3	0.772 7	0.757 7	0.970 3	0.589 7
	SDUNet	0.774 4	0.752 8	0.763 4	0.969 8	0.605 8
	CAS-DeepNet	0.743 5	0.789 2	0.765 6	0.976 4	0.578 0

模型	Params	MACs	Time/s
U-Net++	37.65×10⁶	186.43×10⁹	1.23
D-LinkNet	72.63×10⁶	133.23×10⁹	1.48
RoadNet	65.35×10⁶	167.46×10⁹	1.57
DeepLabV3+	71.42×10⁶	233.45×10⁹	1.82
ACNet	47.39×10⁶	192.36×10⁹	1.69
SDUNet	76.63×10⁶	265.67×10⁹	1.73
CAS-DeepNet	68.34×10⁶	243.82×10⁹	1.71

试验	CEEM	CA	ECA	CS-ASPP	Mob-DeepLabV3+	DeepLabV3+	Pre	Rec	F₁	OA	IoU
1	√				√		0.748 2	0.773 1	0.760 4	0.964 5	0.613 3
2		√	√		√		0.754 9	0.794 6	0.794 5	0.955 4	0.619 7
3				√	√		0.761 1	0.769 3	0.765 2	0.957 1	0.619 8
4	√	√	√	√	√		0.781 2	0.860 5	0.818 9	0.974 4	0.693 4
5					√		0.711 0	0.769 4	0.739 0	0.961 1	0.586 1
6						√	0.738 8	0.810 0	0.772 8	0.960 5	0.629 7

模型	Pre	Rec	F₁	OA	IoU
Prewitt+Mob-DeepLabV3+	0.739 8	0.732 3	0.736 0	0.957 6	0.582 4
Laplacian+Mob-DeepLabV3+	0.744 2	0.740 8	0.742 5	0.961 0	0.590 5
Robert+Mob-DeepLabV3+	0.745 8	0.760 9	0.753 2	0.958 0	0.604 2
Sobel+Mob-DeepLabV3+	0.747 1	0.768 7	0.757 7	0.957 5	0.610 0
Canny+Mob-DeepLabV3+	0.748 2	0.773 1	0.760 4	0.964 5	0.613 3