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

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

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.

Original languageEnglish
Pages (from-to)2069-2083
Number of pages15
JournalRemote Sensing
Volume6
Issue number3
DOIs
StatePublished - 2014

Keywords

  • Ensemble empirical mode decomposition (EEMD)
  • Hyperspectral
  • Remote sensing
  • Similarity measurement
  • Spectral angle mapper

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