SLCRF: Subspace Learning With Conditional Random Field for Hyperspectral Image Classification

Subspace learning (SL) plays an essential role in hyperspectral image (HSI) classification since it can provide an effective solution to reduce the redundant information in the image pixels of HSIs. Previous works about SL aim to improve the accuracy of HSI recognition. Using a large number of label...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 59; H. 5; S. 4203 - 4217
Hauptverfasser:	Cao, Yun, Mei, Jie, Wang, Yuebin, Zhang, Liqiang, Peng, Junhuan, Zhang, Bing, Li, Lihua, Zheng, Yibo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classification Conditional random field (CRF) Conditional random fields Data models Decoding hyperspectral image (HSI) classification Hyperspectral imaging Image classification Iterative algorithms Iterative methods Labour Learning Linear programming Objective function optimization Pixels Probability theory relationship construction Spatial data subspace learning (SL) Training
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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