结合注意力与上下文融合的遥感图像道路提取

doi:10.13229/j.cnki.jdxbgxb.20240442

Abstract

Abstract:

Aiming at the complexity of features in remote sensing images and the existence of an elongated and continuous distribution of roads that are easy to obscure， a Road Extraction Model for Remote Sensing Images Combining Attention and Context Fusion （ACFD-LinkNet） was proposed. The network is based on the D-LinkNet network. Firstly， a strip attention module was used in the codec part of the D-LinkNet network to enhance the feature extraction capability of roads at different scales， to better capture the global features of the roads， and to capture the long-distance information of the roads. Secondly， a Context Fusion Module （CFM） was proposed and added to the feature delivery part of the network codec to predict road connections between neighboring pixels， fusing road information between different layers of the context to solve the problem of obstacle obstruction interfering with road connections. Finally， the cross-entropy loss function and Dice loss function of the improved model were set up with multiple loss function hyperparameter weight assignments to solve the dataset positive and negative sample inhomogeneity， and the optimal segmentation accuracy was obtained by adjusting the weight ratios. Experiments on the DeepGlobe and CHN6-CUG datasets resulted in F₁ values of 86.76% and 92.12% for the composite metrics， respectively， which is an improvement of 3.96% and 1.13% compared to the D-LinkNet model， in addition to optimal performance compared to semantic segmentation methods such as Unet， Deeplabv3+， A²-FPN， etc.

Key words: image processing, remote sensing image, road extraction, attention mechanism, context feature fusion, hyperparameter weight allocation

CLC Number:

TP751

Yun-hong LI,Mei WANG,Xue-ping SU,Li-min LI,Fu-xing ZHANG,Te-ji HAO. Road extraction from remote sensing images combining attention and context fusion[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(12): 4034-4044.

Figures/Tables 13

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Table 1

Performance comparison of loss functions with different weight ratios"

$α : β$	mIoU
$α : β$	DeepGlobe数据集	CHN6-CUG数据集
1∶1	79.32	74.94
1∶2	79.38	75.40
1∶3	79.52	76.66
1∶4	79.68	77.90
1∶5	79.39	76.49

Table 1

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Table 2

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Fig.9

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方法	DeepGlobe数据集
方法	Precision	Recall	F₁-score	mIoU
Unet^［8］	94.63	77.33	85.11	75.82
FCN^［6］	82.44	77.87	80.09	75.11
Deeplabv3+^［17］	90.94	76.90	83.33	75.84
D-LinkNet^［9］	94.11	73.93	82.80	78.23
SGCN^［18］	94.86	53.38	68.31	74.67
A²-FPN^［19］	92.40	78.38	84.81	78.21
EGE-UNet^［20］	76.93	69.44	72.99	65.59
VM-UNet^［21］	78.10	71.55	74.68	75.28
ACFD-LinkNet	95.79	79.29	86.76	79.68

方法	CHN6-CUG数据集
方法	Precision	Recall	F₁-score	mIoU
Unet^［10］	78.56	88.30	83.15	73.56
FCN^［8］	90.83	90.97	90.90	74.94
Deeplabv3+^［19］	90.98	90.35	90.66	75.70
D-LinkNet^［11］	90.97	91.01	90.99	76.66
SGCN^［20］	88.97	52.64	66.14	67.10
A²-FPN^［21］	90.19	78.42	83.89	75.89
EGE-UNet^［22］	48.91	49.99	49.45	60.91
VM-UNet^［23］	73.33	61.95	67.17	67.86
ACFD-LinkNet	92.22	92.02	92.12	77.90

方法	SAM数量	嵌入位置	DeepGlobe数据集							CHN6-CUG数据集
方法	SAM数量	嵌入位置	Precision	Recall		F₁-score		mIoU	Precision	Recall	F₁-score		mIoU
D-LinkNet	-	-	94.11	73.93	82.80		78.23		90.97	91.01		90.99	76.66
D-LinkNet+SAM	1	空洞卷积左侧	94.68	77.45	85.20		79.25		91.52	91.93		91.72	77.05
D-LinkNet+SAM	1	空洞卷积右侧	95.54	76.14	84.74		78.43		90.18	90.69		90.44	75.97
D-LinkNet+SAM	2	空洞卷积两侧	94.61	72.39	82.02		78.03		90.11	89.63		89.87	75.40
D-LinkNet+CFM	-	-	94.70	78.17	85.64		79.19		91.60	91.40		91.50	77.09
ACFD-LinkNet	-	-	95.79	79.29	86.76		79.68		92.22	92.02		92.12	77.90

Road extraction from remote sensing images combining attention and context fusion

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