Unsupervised Change Detection Using Convolutional-Autoencoder Multiresolution Features

The use of deep learning (DL) methods for change detection (CD) is currently dominated by supervised models that require a large number of labeled samples. However, these samples are difficult to acquire in the multitemporal case. A possible alternative is leveraging methods that exploit transfer le...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 19
Hauptverfasser:	Bergamasco, Luca, Saha, Sudipan, Bovolo, Francesca, Bruzzone, Lorenzo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Change detection Convolutional autoencoder (CAE) Data models Decoding Deep learning deep learning (DL) Deforestation Detection Feature extraction Feature maps Image acquisition Landsat Landsat satellites Methods multitemporal analysis Remote sensing remote sensing (RS) Satellite imagery Semantics Task analysis Training Transfer learning unsupervised change detection (CD) unsupervised learning
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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