A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 55, Issue 7, 2017, Pages 4032-4046

  Sun, Weiwei ^a,b   Yang, Gang ^a   Du, Bo ^b   Zhang, Lefei ^b   Zhang, Liangpei ^b  

^a NINGBO UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

Classification; Dimensionality reduction; Feature extraction; Hyperspectral imagery (HSI); Sparse and low rank near isometric linear embedding (SLRNILE)

Indexed keywords

COMPUTATION THEORY; CONVEX OPTIMIZATION; EXTRACTION; FEATURE EXTRACTION; IMAGE CLASSIFICATION; LAGRANGE MULTIPLIERS; LINEAR PROGRAMMING; MAPPING; MATRIX ALGEBRA; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SPECTROSCOPY;

ALTERNATING DIRECTION METHOD OF MULTIPLIERS; DIMENSIONALITY REDUCTION; FEATURE EXTRACTION METHODS; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL IMAGERY CLASSIFICATIONS; LOCALITY PRESERVING PROJECTIONS; NEIGHBORHOOD PRESERVING EMBEDDING; OPTIMIZATION PROCEDURES;

CLASSIFICATION (OF INFORMATION);

DATA SET; IMAGE CLASSIFICATION; MAPPING; MATRIX; NUMERICAL METHOD; OPTIMIZATION; PIXEL; REMOTE SENSING; SATELLITE DATA; SPECTRAL ANALYSIS; STATISTICAL CONCEPT;

EID: 85018522896     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2017.2686842     Document Type: Article

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