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

Hsuan Ren, Yung Ling Wang, Min Yu Huang, Yang Lang Chang, Hung Ming Kao

研究成果: 雜誌貢獻期刊論文同行評審

22 引文 斯高帕斯(Scopus)

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.

原文???core.languages.en_GB???
頁(從 - 到)2069-2083
頁數15
期刊Remote Sensing
6
發行號3
DOIs
出版狀態已出版 - 2014

指紋

深入研究「Ensemble empirical mode decomposition parameters optimization for spectral distance measurement in hyperspectral remote sensing data」主題。共同形成了獨特的指紋。

引用此