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

NCU Institutional Repository > 研究中心 > 太空及遙測研究中心 > 期刊論文 > Item 987654321/101253

請使用永久網址來引用或連結此文件: https://ir.lib.ncu.edu.tw/handle/987654321/101253

題名:	Ensemble empirical mode decomposition parameters optimization for spectral distance measurement in hyperspectral remote sensing data
作者:	任玄;Ren, Hsuan;Wang, Yung-Ling;Huang, Min-Yu;Chang, Yang-Lang;Kao, Hung-Ming
貢獻者:	太空及遙測研究中心
關鍵詞:	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
日期:	2014-01-01
上傳時間:	2026-04-21 14:28:28 (UTC+8)
出版者:	MDPI Multidisciplinary Digital Publishing Institute;Basel: MDPI AG
摘要:	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
顯示於類別:	[太空及遙測研究中心] 期刊論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
index.html		0Kb	HTML	19	檢視/開啟

在NCUIR中所有的資料項目都受到原著作權保護.

社群 sharing

資料載入中.....