Conjoint Cross-Attention Modeling and Joint Feature Calibrating for Remote Sensing Image Change Detection via a Triple-Double Network-Reference-Cited by-同舟云学术

Conjoint Cross-Attention Modeling and Joint Feature Calibrating for Remote Sensing Image Change Detection via a Triple-Double Network

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:

Liu Fang¹^ORCID,An Jiaqi¹,Liu Jia¹^ORCID,Yang Jingxiang¹^ORCID,Tang Xu²^ORCID,Xiao Liang¹^ORCID

Affiliation:

1. School of Computer Science and Engineering, Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing, China

2. School of Artificial Intelligence, Xidian University, Xi’an, China

Funder

National Nature Science Foundations of China

Jiangsu Geological Bureau Research Project

Fundamental Research Funds for the Central Universities

Open Research Fund Program of State Key Laboratory of Information Engineering in Surveying

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10508632.pdf?arnumber=10508632

Reference47 articles.

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

2. Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects

3. Improving Land Cover Change Detection and Classification With BRDF Correction and Spatial Feature Extraction Using Landsat Time Series: A Case of Urbanization in Tianjin, China

4. A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment

5. Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set