Hyperspectral data discrimination based on Ensemble Empirical Mode Decomposition

Ming Shu Wang*, Tee-Ann Teo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The classification of hyperspectral data is an important issue. This investigation adopts a novel hyperspctral data classification approach using Ensemble Empirical Mode Decomposition (EEMD). First, the EEMD is applied to decompose the spectra into several components. Then, some selected components are applied to generate the classification indices. The classification indices include correlation coefficients, weighted Euclidean distance and weighted absolute distance. Two spectrum data sets are selected in the experiment. The first concerns vegetation while the other is about soils. The experiment results demonstrate that EEMD can characterize the spectral properties. Moreover, the decomposed components are able to separate the spectrum data when different indices are applied. The proposed method enhances hyperspecral data discrimination of different classes. The recognition rate are from 8.00% to 195.33%, 37.53% to 531.37%, and 26.31% to 423.84%; and are measured by correlation coefficients, weighted Euclidean distance and weighted absolute distance, respectively.

Original languageEnglish
Title of host publication2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Proceedings
Pages385-388
Number of pages4
DOIs
StatePublished - 5 Sep 2011
Event2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Nanjing, China
Duration: 24 Jun 201126 Jun 2011

Publication series

Name2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Proceedings

Conference

Conference2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011
CountryChina
CityNanjing
Period24/06/1126/06/11

Keywords

  • Ensemble Empirical Mode Decomposition
  • Hyperspectral imaging
  • Image classification
  • Remote sensing

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