Open-Source Data-Driven Cross-Domain Road Detection From Very High Resolution Remote Sensing Imagery-Reference-Cited by-同舟云学术

Open-Source Data-Driven Cross-Domain Road Detection From Very High Resolution Remote Sensing Imagery

Published:2022 Issue: Volume:31 Page:6847-6862
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Lu Xiaoyan¹^ORCID,Zhong Yanfei²^ORCID,Zhang Liangpei²^ORCID

Affiliation:

1. Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA

2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/9626658/09931623.pdf?arnumber=9931623

Reference61 articles.

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2. Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline

3. Unsupervised Adversarial Domain Adaptation Network for Semantic Segmentation

4. Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images

5. Learning to Adapt Structured Output Space for Semantic Segmentation

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