Road-SAM: Adapting the Segment Anything Model to Road Extraction From Large Very-High-Resolution Optical Remote Sensing Images-Reference-Cited by-同舟云学术

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Road-SAM: Adapting the Segment Anything Model to Road Extraction From Large Very-High-Resolution Optical Remote Sensing Images

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Feng Wenqing¹^ORCID,Guan Fangli¹,Sun Chenhao²,Xu Wei³

Affiliation:

1. Computer and Software School, Hangzhou Dianzi University, Hangzhou, China

2. Electrical and Information Engineering School, Changsha University of Science and Technology, Changsha, China

3. Information System and Management College, National University of Defense Technology, Changsha, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/8859/10365397/10613866.pdf?arnumber=10613866

Reference18 articles.

1. Road extraction in remote sensing data: A survey

2. Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

3. Unsupervised road extraction via a Gaussian mixture model with object-based features

4. Numerical experiments with cooperating multiple quadratic snakes for road extraction

5. LinkNet: Exploiting encoder representations for efficient semantic segmentation

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