Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning

IEEE Transactions on Geoscience and Remote Sensing

Volumn 54, Issue 5, 2016, Pages 2615-2626

  Persello, Claudio ^a   Bruzzone, Lorenzo ^b  

^a UNIVERSITY OF TWENTE   (Netherlands)

^b UNIVERSITY OF TRENTO   (Italy)

Author keywords

Domain adaptation; feature selection; hyperspectral images; image classification; kernel methods; remote sensing; support vector machines (SVMs); transfer learning

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); IMAGE CLASSIFICATION; MULTIOBJECTIVE OPTIMIZATION; OPTIMIZATION; PARETO PRINCIPLE; SPECTROSCOPY;

CONDITIONAL DISTRIBUTION; FEATURE SELECTION METHODS; FUNCTIONAL RELATIONSHIP; GENERALIZATION CAPABILITY; HYPER-SPECTRAL IMAGES; NONPARAMETRIC APPROACHES; PARETO OPTIMAL SOLUTIONS; REPRODUCING KERNEL HILBERT SPACES;

FEATURE EXTRACTION;

ALGORITHM; DOMAIN STRUCTURE; IMAGE ANALYSIS; IMAGE CLASSIFICATION; MACHINE LEARNING; OPTIMIZATION; REMOTE SENSING; SATELLITE IMAGERY; SUPPORT VECTOR MACHINE;

EID: 84951310856     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2015.2503885     Document Type: Article

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