Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks-Reference-Cited by-同舟云学术

Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks

Published:2024 Issue: Volume:62 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Chang Shizhen¹^ORCID,Kopp Michael¹^ORCID,Ghamisi Pedram²^ORCID,Du Bo³^ORCID

Affiliation:

1. Institute of Advanced Research in Artificial Intelligence, Vienna, Austria

2. Machine Learning Group, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany

3. Hubei Key Laboratory of Multimedia and Network Communication Engineering, National Engineering Research Center for Multimedia Software, School of Computer Science, Institute of Artificial Intelligence, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

Innovative Research Group Project of Hubei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10587266.pdf?arnumber=10587266

Reference71 articles.

1. A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images

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

3. A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors

4. Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters

5. Detection and analysis of change in remotely sensed imagery with application to wide area surveillance