NDLI: Effect of Continuum Removal on Hyperspectral Coastal Vegetation Classification Using a Fuzzy Learning Vector Quantizer

Content Provider	IEEE Xplore Digital Library
Author	Filippi, A.M. Jensen, J.R.
Copyright Year	1980
Abstract	Continuum removal (CR) is often used for geologic mapping; however, more research is needed to better establish the utility of CR for vegetation classification, particularly when used with artificial neural networks (ANNs). In this paper, fuzzy learning vector quantization (FLVQ) was applied to hyperspectral Airborne Visible/Infrared Imaging Spectrometer imagery for coastal vegetation classification. FLVQ performance was compared with that of a multilayer perceptron (MLP), a self-organizing map (SOM), and an endmember-based algorithm [spectral feature fitting (SFF)]. The objective was to assess the effect of CR as an input vector-preprocessing step for ANN model development on classification accuracy. Compared with a related study, continuum intact (CI) reflectance data generally yielded higher classification accuracies than those based on CR. Thus, CR may not be a preferred preprocessing method for coastal vegetation mapping over broad wavelength ranges. MLP slightly outperformed FLVQ when applied to CI data, but FLVQ yielded higher accuracy than MLP with CR. However, there was no significant difference between them for both data treatments at the 95% confidence level. All ANNs tested yielded significantly higher classification accuracies than SFF. For model development, the 588-neuron FLVQ required only 8.2% of MLP training time, 27.8% of the 400-neuron SOM time, and 8.8% of the 729-neuron 3-D SOM time
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	1857
Ending Page	1869
Page Count	13
File Size	1028306
File Format	PDF
ISSN	01962892
Volume Number	45
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sea measurements Chromium Vegetation mapping Hyperspectral imaging Artificial neural networks Geology Vector quantization Infrared imaging Infrared spectra Spectroscopy wetlands Continuum removal (CR) fuzzy neural networks hyperspectral learning vector quantization (LVQ) remote sensing
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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