Supervised Dimensionality Reduction of Hyperspectral Imagery Via Local and Global Sparse Representation

Despite the successful applications of unsupervised sparse dimensionality reduction (USDR) in pattern recognition, the USDR still suffers from two challenges for hyperspectral images (HSIs), which limit its discriminative performance: first, it cannot be applied for dimensionality reduction using bo...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.3860-3874
Hauptverfasser:	Cao, Faxian, Yang, Zhijing, Hong, Xiaobin, Cheng, Yongqiang, Huang, Yuezhen, Lv, Jujian
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Dimensionality reduction Feature extraction Hyperspectral images (HSIs) Hyperspectral imaging Imagery Information processing local and global dimensionality reduction sparse representation (LGDRSR) Object recognition Pattern recognition Principal component analysis Reduction Redundancy Representations Spatial data spectral and spatial information Testing Training unsupervised sparse dimensionality reduction (USDR)
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