Sparse Hyperspectral Band Selection Based on Expectation Maximization

Hyperspectral band selection seeks to identify a compact subset of informative spectral channels that preserves task-relevant information while mitigating the storage, transmission, and computational burdens imposed by high-dimensional data. Yet prevailing techniques face two pervasive limitations:...

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Veröffentlicht in:	IEEE transactions on circuits and systems for video technology S. 1
Hauptverfasser:	Gao, Likun, Xue, Xinhui, Zheng, Haowen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 2025
Schlagworte:	Clustering algorithms Computational efficiency Computational modeling Correlation EM algorithm Encoding Faces Hyperspectral band selection hyperspectral image classification Hyperspectral imaging Redundancy sparse learning Training Transformers
ISSN:	1051-8215, 1558-2205
Online-Zugang:	Volltext
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