A CNN Framework With Slow-Fast Band Selection and Feature Fusion Grouping for Hyperspectral Image Change Detection

Change detection approaches can detect changed areas of the same scene at different times. Hyperspectral remote-sensing images contain large amounts of spectral information at high resolution. As hyperspectral datasets become abundant, more and more change detection technologies use hyperspectral im...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 16
Hauptverfasser:	Ou, Xianfeng, Liu, Liangzhen, Tu, Bing, Zhang, Guoyun, Xu, Zhi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Change detection Change detection algorithms Convolutional neural networks Data mining Datasets Deep learning Detection difference matrix Feature extraction feature fusion grouping (FFG) hyperspectral image Hyperspectral imaging Redundancy Remote sensing slow-fast band selection (SFBS) Tensors
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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