SAM-Assisted Remote Sensing Imagery Semantic Segmentation With Object and Boundary Constraints-Reference-Cited by-同舟云学术

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SAM-Assisted Remote Sensing Imagery Semantic Segmentation With Object and Boundary Constraints

Published:2024 Issue: Volume:62 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ma Xianping¹^ORCID,Wu Qianqian¹^ORCID,Zhao Xingyu¹,Zhang Xiaokang²^ORCID,Pun Man-On¹^ORCID,Huang Bo³

Affiliation:

1. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China

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

3. Department of Geography, The University of Hong Kong, Hong Kong, SAR, China

Funder

National Natural Science Foundation of China

Guangdong Provincial Key Laboratory of Future Networks of Intelligence

Guangdong Basic and Applied Basic Research Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10636322.pdf?arnumber=10636322

Reference75 articles.

1. Deep learning in environmental remote sensing: Achievements and challenges

2. A coarse-to-fine weakly supervised learning method for green plastic cover segmentation using high-resolution remote sensing images

3. A Cross-Domain Object-Semantic Matching Framework for Imbalanced High Spatial Resolution Imagery Water-Body Extraction

4. Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery

5. Land cover classification from remote sensing images based on multi-scale fully convolutional network

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