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

被引：0
|
作者：
Chen X. [1 ,2 ]
Liu Z. [1 ,2 ]
Zhou S. [1 ]
Yu H. [1 ]
Liu Y. [1 ]
机构：
[1] School of Aerospace Science and Technology, Xidian University, Xi'an
[2] Key Laboratory of Mine Geological Hazards Mechanism and Control, Ministry of Natural Resources, Xi'an
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 04期
关键词：
deep learning; high-resolution net (HRNet); non-local block; remote sensing image;
D O I：
10.12305/j.issn.1001-506X.2024.04.04
中图分类号：
学科分类号：
摘要：
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. © 2024 Chinese Institute of Electronics. All rights reserved.
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页码：1167 / 1173
页数：6
相关论文
共 38 条
  • [1] LIU Y, KANG J, GUAN H Y, Et al., Road extraction model of high-resolution remote sensing images based on dual-attention residual network, Journal of Geo-Information Science, 25, 2, pp. 396-408, (2023)
  • [2] LIANG Q Y, WANG J L, LI P F, Et al., Research on natural roads extraction in arid and semiarid regions of the Mongolian Plateau based on GF-1 images-take the Gurvantes Soum, Mongolia as an example, Remote Sensing for Natural Resources, 35, 2, pp. 122-131, (2022)
  • [3] DAI L, ZHANG G Y, ZHANG R T, RADANet: road augmented deformable attention network for road extraction from complex high-resolution remote-sensing images, IEEE Trans.on Geoscience and Remote Sensing, 61, (2023)
  • [4] ZHANG X, ZHANG C K, LI H M, Et al., A road extraction method based on high resolution remote sensing image, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 671-676, (2020)
  • [5] LIU L Y, DONG Y Y, HUANG W J, Et al., Enhanced regional monitoring of wheat powdery mildew based on an instance-based transfer learning method, Remote Sensing, 11, 3, (2019)
  • [6] MEGHANA I, MEGHANA J D N V L, JAYARAMAN R., Smart attendance management system using radio frequency identification, Proc. of the International Conference on Communication and Signal Processing, pp. 1045-1049, (2020)
  • [7] DAI J G, WANG Y, DU Y, Et al., Development and prospect of road extraction method for optical remote sensing image, National Remote Sensing Bulletin, 24, 7, pp. 804-823, (2020)
  • [8] DAI J G, MA R C, GONG L T, Et al., A model-driven-to-sample-driven method for rural road extraction, Remote Sensing, 13, 8, (2021)
  • [9] LIN X G, ZHANG R, SHEN J, A template-matching based approach for extraction of roads from very high-resolution remotely sensed imagery, International Journal of Image and Data Fusion, 3, 2, pp. 149-168, (2012)
  • [10] TAN H, SHEN Z M, DAI J G, Semi-automatic extraction of rural roads under the constraint of combined geometric and texture features, ISPRS International Journal of Geo-Information, 10, 11, (2021)
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