NDLI: Hyperspectral data discrimination based on Ensemble Empirical Mode Decomposition

Content Provider	IEEE Xplore Digital Library
Author	Ming-Shu Wang Tee-Ann Teo
Copyright Year	2011
Description	Author affiliation: Department of Civil Engineering, National Chiao Tung University, Hsinchu, Taiwan (Tee-Ann Teo) \|\| School of Geographic and Oceanographic Sciences, Nanjing University, China (Ming-Shu Wang)
Abstract	The classification of hyperspectral data is an important issue. This investigation adopts a novel hyperspectral 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.
Starting Page	385
Ending Page	388
File Size	614430
Page Count	4
File Format	PDF
ISBN	9781424491728
e-ISBN	9781424491711
DOI	10.1109/RSETE.2011.5964294
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Ensemble Empirical Mode Decomposition Euclidean distance Vegetation White noise Remote sensing Hyperspectral imaging Image classification
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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