基于HRNet的高分辨率遥感影像道路提取方法

doi:10.12305/j.issn.1001-506X.2024.04.04

Abstract

Abstract:

In response to the problems of low accuracy and weak robustness in traditional road extraction from high-resolution remote sensing images, a high-resolution net (HRNet) based approach is proposed to achieve road segmentation in high-resolution remote sensing images. This article improves HRNet by concatenating the output of HRNet subnets of the same resolution with the output layer results and inputting the above into non-local block. The two loss functions, Cross-entropy Loss and Dice Loss, are used to solve the problem of imbalanced road dataset samples. The experimental results show that the improved HRNet performs better in road extraction on the publicly available CHN6-CUG road dataset compared to other methods, achieving 97.65%, 84.91%, and 97.25% respectively in recall, mean intersection over union (MIoU), and F1 score.

Key words: high-resolution net (HRNet), non-local block, remote sensing image, deep learning

CLC Number:

TP751

Xuemei CHEN, Zhiheng LIU, Suiping ZHOU, Hang YU, Yanming LIU. Road extraction from high-resolution remote sensing images based on HRNet[J]. Systems Engineering and Electronics, 2024, 46(4): 1167-1173.

Figures/Tables 8

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References 38

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网络模型	Recall	Precision	MIoU	F1
HRNet	94.80	98.19	79.28	96.46
本文方法-N^*	96.24	95.56	83.25	96.81
本文方法	97.26	96.85	84.91	97.25

网络模型	Recall	Precision	MIoU	F1
U-Net	95.55	97.42	80.72	96.47
FCN	93.37	98.09	73.97	95.66
PSPNet	93.56	98.59	75.59	96.01
DeeplabV3+	95.08	98.21	79.96	96.62
D-LinkNet50	89.71	87.45	54.16	87.91
HRNet	94.80	98.19	79.28	96.46
本文方法	97.26	96.85	84.91	97.25

网络模型	参数量/MB	计算量	预测时间/s
U-Net	13.40	124.49	0.49
FCN	23.64	28.99	0.20
PSPNet	46.71	184.73	0.59
DeeplabV3+	40.47	76.54	0.55
D-LinkNet50	217.65	120.39	0.78
HRNet	9.64	18.66	0.24
本文方法	12.38	60.46	0.46

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Road extraction from high-resolution remote sensing images based on HRNet

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