Optimal transport-based domain adaptation for semantic segmentation of remote sensing images-Reference-Cited by-同舟云学术

Optimal transport-based domain adaptation for semantic segmentation of remote sensing images

Published:2024-01-16 Issue:2 Volume:45 Page:420-450
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Shen Ziyang¹²³,Ni Huan¹²³^ORCID,Guan Haiyan¹²³^ORCID,Niu Xiaonan⁴

Affiliation:

1. School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing, China

2. Technology Innovation Center of Integration Applications in Remote Sensing and Navigation, Ministry of Natural Resources, Nanjing, China

3. Jiangsu Engineering Center for Collaborative Navigation/Positioning and Smart Applications, Nanjing, China

4. Nanjing Center, China Geological Survey, Nanjing, China

Funder

Beijing Key Laboratory of Advanced Optical Remote Sensing Technology

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2297176

Reference51 articles.

1. Unsupervised Domain Adaptation With Optimal Transport in Multi-Site Segmentation of Multiple Sclerosis Lesions From MRI Data

2. Arjovsky, M., S. Chintala, and L. Bottou. 2017, July. “Wasserstein generative adversarial networks.” In International conference on Machine Learning, Sydney, Australia, 214–223. PMRL.

3. Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

4. Biasetton, M., U. Michieli, G. Agresti, and P. Zanuttigh. 2019. “Unsupervised domain adaptation for semantic segmentation of urban scenes.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CA, USA, 1211–1220.

5. Displacement interpolation using Lagrangian mass transport