Combined FATEMD‐based band selection method for hyperspectral images. Issue 2 (1st February 2019)

• Record Type: Journal Article
• Authors: Yu, Wenbo; Zhang, Miao; Shen, Yi
• Is Part Of: IET image processing. Volume 13:Issue 2(2019)
• Page Start: 287
• Page End: 298
• Publication Date: 2019-02-01
• Subjects: hyperspectral imaging -- image classification -- feature selection -- remote sensing -- geophysical image processing -- pattern clustering; combined FATEMD‐based band selection method -- hyperspectral images -- feature selection -- hyperspectral data -- hyperspectral band selection method -- combined fast‐and‐adaptive tridimensional empirical mode decomposition -- cFATEMD -- tridimensional intrinsic mode functions -- TIMF -- k‐means clustering algorithm -- Dunn validity index -- RES -- spectral similarity -- residual -- publicly available hyperspectral datasets; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5550
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.