NDLI: Combined FATEMD-based band selection method for hyperspectral images

Content Provider	IET Digital Library
Author	Yu, Wenbo Zhang, Miao Shen, Yi
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.
Starting Page	287
Ending Page	298
Page Count	12
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5550
Journal	IET Image Processing
Publisher Date	2018-07-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CFATEMD Combined Fast-and-adaptive Tridimensional Empirical Mode Decomposition Combined FATEMD-based Band Selection Method Computer Vision And Image Processing Technique Data And Information; Acquisition, Processing, Storage And Dissemination in Geophysics Dunn Validity Index Feature Selection Geography And Cartography Computing Geophysical Image Processing Geophysical Technique And Equipment Hyperspectral Band Selection Method Hyperspectral Data Hyperspectral Image Image Classification Image Recognition Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research K-means Clustering Algorithm Pattern Clustering Publicly Available Hyperspectral Datasets Remote Sensing RES Residual Spectral Similarity TIMF Tridimensional Intrinsic Mode Function
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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