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Remote Sens. 2014, 6(3), 2069-2083; doi:10.3390/rs6032069
Article

Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data

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Received: 12 December 2013; in revised form: 5 February 2014 / Accepted: 10 February 2014 / Published: 7 March 2014
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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Abstract: 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.
Keywords: hyperspectral; remote sensing; ensemble empirical mode decomposition (EEMD); spectral angle mapper; similarity measurement hyperspectral; remote sensing; ensemble empirical mode decomposition (EEMD); spectral angle mapper; similarity measurement
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Ren, H.; Wang, Y.-L.; Huang, M.-Y.; Chang, Y.-L.; Kao, H.-M. Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data. Remote Sens. 2014, 6, 2069-2083.

AMA Style

Ren H, Wang Y-L, Huang M-Y, Chang Y-L, Kao H-M. Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data. Remote Sensing. 2014; 6(3):2069-2083.

Chicago/Turabian Style

Ren, Hsuan; Wang, Yung-Ling; Huang, Min-Yu; Chang, Yang-Lang; Kao, Hung-Ming. 2014. "Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data." Remote Sens. 6, no. 3: 2069-2083.


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