Open Access iconOpen Access

ARTICLE

Segmentation of Remote Sensing Images Based on U-Net Multi-Task Learning

Ni Ruiwen1, Mu Ye1,2,3,4,*, Li Ji1, Zhang Tong1, Luo Tianye1, Feng Ruilong1, Gong He1,2,3,4, Hu Tianli1,2,3,4, Sun Yu1,2,3,4, Guo Ying1,2,3,4, Li Shijun5,6, Thobela Louis Tyasi7

1 College of Information Technology, Jilin Agricultural University, Changchun, 130118, China
2 Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center, Changchun, 130118, China
3 Jilin Province Intelligent Environmental Engineering Research Center, Changchun, 130118, China
4 Jilin Province Information Technology and Intelligent Agriculture Engineering Research Center, Changchun, 130118, China
5 College of Information Technology, Wuzhou University, Wuzhou, 543003, China
6 Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou, 543003, China
7 Department of Agricultural Economics and Animal Production, University of Limpopo, Sovenga, Polokwane, 0727, South Africa

* Corresponding Author: Mu Ye. Email: email

Computers, Materials & Continua 2022, 73(2), 3263-3274. https://doi.org/10.32604/cmc.2022.026881

Abstract

In order to accurately segment architectural features in high-resolution remote sensing images, a semantic segmentation method based on U-net network multi-task learning is proposed. First, a boundary distance map was generated based on the remote sensing image of the ground truth map of the building. The remote sensing image and its truth map were used as the input in the U-net network, followed by the addition of the building ground prediction layer at the end of the U-net network. Based on the ResNet network, a multi-task network with the boundary distance prediction layer was built. Experiments involving the ISPRS aerial remote sensing image building and feature annotation data set show that compared with the full convolutional network combined with the multi-layer perceptron method, the intersection ratio of VGG16 network, VGG16 + boundary prediction, ResNet50 and the method in this paper were increased by 5.15%, 6.946%, 6.41% and 7.86%. The accuracy of the networks was increased to 94.71%, 95.39%, 95.30% and 96.10% respectively, which resulted in high-precision extraction of building features.

Keywords


Cite This Article

APA Style
Ruiwen, N., Ye, M., Ji, L., Tong, Z., Tianye, L. et al. (2022). Segmentation of remote sensing images based on u-net multi-task learning. Computers, Materials & Continua, 73(2), 3263-3274. https://doi.org/10.32604/cmc.2022.026881
Vancouver Style
Ruiwen N, Ye M, Ji L, Tong Z, Tianye L, Ruilong F, et al. Segmentation of remote sensing images based on u-net multi-task learning. Comput Mater Contin. 2022;73(2):3263-3274 https://doi.org/10.32604/cmc.2022.026881
IEEE Style
N. Ruiwen et al., "Segmentation of Remote Sensing Images Based on U-Net Multi-Task Learning," Comput. Mater. Contin., vol. 73, no. 2, pp. 3263-3274. 2022. https://doi.org/10.32604/cmc.2022.026881



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1512

    View

  • 731

    Download

  • 0

    Like

Share Link