Open AccessMaximum simplex volume: an efficient unsupervised band selection method for hyperspectral image
Author(s) -
Jiang Xuefeng,
Zhang Lin,
Liu Junrui,
Li Shuying
Publication year - 2019
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5143
Subject(s) - hyperspectral imaging , simplex , artificial intelligence , pattern recognition (psychology) , computer science , projection (relational algebra) , lossless compression , greedy algorithm , pixel , image resolution , volume (thermodynamics) , mathematics , selection (genetic algorithm) , computer vision , algorithm , data compression , physics , geometry , quantum mechanics
Hyperspectral imaging makes it possible to obtain object information with fine spectral resolution as well as spatial resolution, which is beneficial to a wide array of applications. However, there is a high correlation among the bands in a hyperspectral image (HSI). Band selection (BS), selecting only some representative bands to describe well the original image, is an appropriate approach to tackle this problem. In this study, the authors propose an efficient greedy‐based unsupervised BS method, namely the maximum simplex volume by orthogonal‐projection BS method. The main contributions are two‐fold: (i) an information‐lossless compressed descriptor in the Euclidean sense that can reduce the amount of redundant information in the band analysis and (ii) an orthogonal‐projection‐based algorithm to find the band points forming the simplex of maximum volume. The experimental results on four real HSIs demonstrate that the proposed method can achieve satisfying pixel classification performances and is computationally fast.