A Sparse and Low-Rank Near-Isometric Linear Embedding Method for Feature Extraction in Hyperspectral Imagery Classification

• Published in: IEEE transactions on geoscience and remote sensing Vol. 55; no. 7; pp. 4032 - 4046
• Main Authors: Sun, Weiwei, Yang, Gang, Du, Bo, Zhang, Lefei, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Computation; Computer applications; Computer memory; Data; dimensionality reduction; Direction; Embedding; Feature extraction; Frameworks; hyperspectral imagery (HSI); Hyperspectral imaging; Image classification; Imagery; Isometric; Lagrange multiplier; Learning systems; Manifolds; Mapping; Methods; Pixels; Principal component analysis; Principal components analysis; Projection; sparse and low-rank near-isometric linear embedding (SLRNILE); Sparse matrices; State of the art

A sparse and low-rank near-isometric linear embedding (SLRNILE) method has been proposed to make dimensionality reduction and extract proper features for hyperspectral imagery (HSI) classification. The SLRNILE stands on the theory of the John-Lindenstrauss lemma, and tries to estimate a sparse and l...