AdaptFormer: An Adaptive Hierarchical Semantic Approach for Change Detection on Remote Sensing Images-Reference-Cited by-同舟云学术

AdaptFormer: An Adaptive Hierarchical Semantic Approach for Change Detection on Remote Sensing Images

Published:2024 Issue: Volume:73 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Huang Teng¹^ORCID,Hong Yile¹^ORCID,Pang Yan¹^ORCID,Liang Jiaming¹^ORCID,Hong Jie¹^ORCID,Huang Lin²^ORCID,Zhang Yuan³^ORCID,Jia Yan⁴^ORCID,Savi Patrizia⁵^ORCID

Affiliation:

1. Institute of Artificial Intelligence, Guangzhou University, Guangzhou, China

2. Department of Engineering and Engineering Technology College of Aerospace, Computing, Engineering, and Design (ACED), Metropolitan State University of Denver, Denver, CO, USA

3. School of Information, North China University of Technology, Beijing, China

4. Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing, China

5. Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy

Funder

National Key Research and Development Program of China

Scientific Research Project for Guangzhou University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10497147.pdf?arnumber=10497147

Reference48 articles.

1. Review Article Digital change detection techniques using remotely-sensed data

2. Land Cover Change Detection With Heterogeneous Remote Sensing Images: Review, Progress, and Perspective

3. Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images

4. Semantic-Aware Dense Representation Learning for Remote Sensing Image Change Detection

5. RaSRNet: An End-to-End Relation-Aware Semantic Reasoning Network for Change Detection in Optical Remote Sensing Images

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