Multi-temporal urban semantic understanding based on GF-2 remote sensing imagery: from tri-temporal datasets to multi-task mapping-Reference-Cited by-同舟云学术

Multi-temporal urban semantic understanding based on GF-2 remote sensing imagery: from tri-temporal datasets to multi-task mapping

Published:2023-08-20 Issue:1 Volume:16 Page:3321-3347
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Shi Sunan¹,Zhong Yanfei¹²,Liu Yinhe¹,Wang Jue¹,Wan Yuting¹,Zhao Ji³,Lv Pengyuan⁴,Zhang Liangpei¹,Li Deren¹

Affiliation:

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, People’s Republic of China

2. Hubei Provincial Engineering Research Center of Natural Resources Remote Sensing Monitoring, Wuhan University, Wuhan, People’s Republic of China

3. College of Computer Science, China University of Geosciences, Wuhan, People’s Republic of China

4. School of Information Engineering, Ningxia University, Yinchuan, People’s Republic of China

Funder

National

Key

Research and Development Program of China

National Natural Science Foundation of China

LIESMARS Special Research Funding

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2246445

Reference50 articles.

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2. Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model

3. Chen Liang-Chieh George Papandreou Iasonas Kokkinos Kevin Murphy and Alan Yuille. 2014. “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.” CoRR. arXiv .

4. TEMDnet: A Novel Deep Denoising Network for Transient Electromagnetic Signal With Signal-to-Image Transformation

5. A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection

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