Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images

Change detection has been a hotspot in the remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detection algorithms have been proposed. Among these methods, image transformation methods with feature extraction and mappi...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 57; H. 12; S. 9976 - 9992
Hauptverfasser:	Du, Bo, Ru, Lixiang, Wu, Chen, Zhang, Liangpei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Analysis Artificial neural networks Change detection Change detection algorithms Components Datasets Deep learning deep network Detection Detection algorithms Eigenvalues and eigenfunctions Feature extraction Image detection Imagery Machine learning Mapping Methods Remote sensing remote sensing images slow feature analysis (SFA) Training Vector analysis
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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