Hyperspectral data discrimination based on Ensemble Empirical Mode Decomposition

Ming-shu Wang, Tee-ann Teo

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

1 Citation (Scopus)

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 hyperspectral 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
Subtitle of host publication24-26 June 2011, Nanjing, China
PublisherIEEE
Pages385-388
ISBN (Electronic)978-1-4244-9171-1
DOIs
Publication statusPublished - Jun 2011
Externally publishedYes
Event2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE) - Nanjing, China
Duration: 24 Jun 201126 Jun 2011

Conference

Conference2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE)
Period24/06/1126/06/11

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  • Cite this

    Wang, M., & Teo, T. (2011). Hyperspectral data discrimination based on Ensemble Empirical Mode Decomposition. In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering: 24-26 June 2011, Nanjing, China (pp. 385-388). IEEE. https://doi.org/10.1109/RSETE.2011.5964294