Ensemble empirical mode decomposition parameters optimization for spectral distance measurement in hyperspectral remote sensing data

NCUIR > Research Center > Center for Space and Remote Sensing Research > journal & Dissertation > Item 987654321/101253

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/101253

Title:	Ensemble empirical mode decomposition parameters optimization for spectral distance measurement in hyperspectral remote sensing data
Authors:	任玄;Ren, Hsuan;Wang, Yung-Ling;Huang, Min-Yu;Chang, Yang-Lang;Kao, Hung-Ming
Contributors:	太空及遙測研究中心
Keywords:	Decomposition;Empirical analysis;ensemble empirical mode decomposition (EEMD);Geological surveys;hyperspectral;Mathematical analysis;Noise standards;Performance evaluation;Pixels;Remote sensing;Similarity;similarity measurement;Spectra;spectral angle mapper
Date:	2014-01-01
Issue Date:	2026-04-21 14:28:28 (UTC+8)
Publisher:	MDPI Multidisciplinary Digital Publishing Institute;Basel: MDPI AG
Abstract:	摘要： This study proposed a new approach to measure the similarity between spectra to discriminate materials and evaluate the performance of parameter-selection procedures. Many pure pixel vector-based similarity measurements have been developed in the past to calculate the distance between two pixel vectors. However, those methods may not be effective since they do not take full advantage of the spectral correlation. In this study, we adopt Ensemble Empirical Mode Decomposition (EEMD) to decompose the spectrum into serial components and employ these components to improve the performance of spectral discrimination. Performance evaluation was conducted with several commonly used measurements, and the spectral samples used for experimentation were provided by the spectral library of United States Geological Survey (USGS). The experimental results have demonstrated that EEMD can extract the spectral features more effectively than common spectral similarity measurements, and it better characterizes spectral properties. Our experimental results also suggest general rules for selecting noise standard deviation, the number of iterations for EEMD and the collection of Intrinsic Mode Functions (IMFs) for classification. Finally, since EEMD is a time-consuming algorithm, we also implement parallel processing with a Graphics Processing Unit (GPU) to increase the processing speed. 出版者： Basel: MDPI AG 出版日期： 2014-03-01 出處： Remote sensing (Basel, Switzerland), 2014-03, Vol.6 (3), p.2069-2083 資源來源： Publicly Available Content Database 版權： Copyright MDPI AG 2014 識別號： ISSN: 2072-4292 識別號： EISSN: 2072-4292 識別號： DOI: 10.3390/rs6032069
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

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