Open AccessCombined FATEMD‐based band selection method for hyperspectral images
Author(s) -
Yu Wenbo,
Zhang Miao,
Shen Yi
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5550
Subject(s) - hyperspectral imaging , pattern recognition (psychology) , computer science , artificial intelligence , cluster analysis , selection (genetic algorithm) , decomposition , residual , feature selection , data set , set (abstract data type) , similarity (geometry) , hilbert–huang transform , data mining , remote sensing , image (mathematics) , algorithm , computer vision , geography , ecology , filter (signal processing) , biology , programming language
Feature selection, which is called band selection for hyperspectral data, is widely used for hyperspectral images. A novel hyperspectral band selection method based on combined fast and adaptive tridimensional empirical mode decomposition (cFATEMD) is proposed in this study. The hyperspectral data is decomposed into a set of tridimensional intrinsic mode functions (TIMFs) and a residual (RES) by FATEMD, which can reduce high‐frequency noise and signal. A stop condition of the decomposition is proposed based on the k‐means clustering algorithm and the Dunn validity index, which can prevent excessive decomposition and make generated RES contain as much useful information as possible. In consideration of the useful information in decomposition results, these TIMFs and the RES are combined into a new data based on the spectral similarity between themselves and the original data. Four state‐of‐the‐art band selection methods, cooperating with the proposed cFATEMD, are used to select bands by the new combined data. Several experiments are conducted on three publicly available hyperspectral datasets and the results are compared with corresponding methods’ results using the original data. Experimental results demonstrate that the proposed method yields great classification appearance.