Style Transformation-Based Spatial-Spectral Feature Learning for Unsupervised Change Detection

Due to the inconsistent imaging environment, the styles of multitemporal multispectral images (MSIs) are quite different, such as image brightness and transparency. For multitemporal MSIs with different styles, the "same object with different spectra" problem is one of the biggest challeng...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 15
Hlavní autori:	Liu, Ganchao, Yuan, Yuan, Zhang, Yuelin, Dong, Yongsheng, Li, Xuelong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Artificial neural networks Attention mechanism Automobiles Change detection Change detection algorithms Detection Detection algorithms Feature extraction Image reconstruction Imaging Imaging techniques Innovation Innovations Machine learning multispectral image (MSI) Neural networks Production facilities Recurrent neural networks Remote sensing Spatial discrimination learning Spectra style transformation (ST) Transparency (optical)
ISSN:	0196-2892, 1558-0644
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