Detecting Changes by Learning No Changes: Data-Enclosing-Ball Minimizing Autoencoders for One-Class Change Detection in Multispectral Imagery-Reference-Cited by-同舟云学术

Detecting Changes by Learning No Changes: Data-Enclosing-Ball Minimizing Autoencoders for One-Class Change Detection in Multispectral Imagery

Published:2022 Issue: Volume:60 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:

Mou Lichao¹^ORCID,Hua Yuansheng¹,Saha Sudipan²^ORCID,Bovolo Francesca³^ORCID,Bruzzone Lorenzo⁴^ORCID,Zhu Xiao Xiang¹^ORCID

Affiliation:

1. Data Science in Earth Observation, Technical University of Munich (TUM), Munich, Germany

2. Chair of Data Science in Earth Observation, Technical University of Munich (TUM), Munich, Germany

3. Fondazione Bruno Kessler, Trento, Italy

4. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

Funder

H2020 European Research Council

Bundesministerium f?r Bildung und Forschung

Bundesministerium f?r Wirtschaft und Energie

Helmholtz Association

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09870684.pdf?arnumber=9870684

Reference86 articles.

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