NDLI: Classification of CASI-3 hyperspectral image by subspace method

Content Provider	IEEE Xplore Digital Library
Author	Hoshino, B. Bagan, H. Nakazawa, A. Kaneko, M. Kawai, M. Yabuki, T.
Copyright Year	2011
Description	Author affiliation: Asia Air Survey Co. Ltd., Kanagawa Prefecture 215-0004, Japan (Nakazawa, A.) \|\| Earth Remote Sensing Data Analysis Center, Tokyo, 104-0054, Japan (Kawai, M.) \|\| Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba-city, Ibaraki, 305-8506, Japan (Bagan, H.) \|\| Department of Biosphere and Environmental Sciences, Rakuno Gakuen University, Bunkyoudai Midorimachi 582, Ebetsu city, Hokkaido, 069-8501, Japan (Hoshino, B.; Kaneko, M.; Yabuki, T.)
Abstract	This study presents a supervised subspace learning classification method which can be applied directly to the original set of spectral bands of hyperspectral data for land cover classification purpose. The CLAss-Featuring Information Compression (CLAFIC) method is used to generate the appropriate feature subspace for each class on the training data set by Karhunen-Loève transform (also known as the principal component analysis). Then, using the iterative learning technology of averaged learning subspace methods (ALSM) to rotate the subspaces slowly for optimizes the subspaces to get better classification accuracy. We carried out experiments with 68 spectral bands Compact Airborne Spectrographic Imager-3 (CASI-3) data set. Experimental results show that Subspace method is a valid and effective alternative to other pattern recognition approaches for the mapping grass species and monitoring grass health using hyperspectral remote sensing data. Moreover, it is worth noting that the ALSMs are easily applied (i.e. they only request to set two parameters and can be directly applied to hyperspectral data) and they can entirely identify the training samples in a finite number of steps.
Starting Page	724
Ending Page	727
File Size	583411
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049232
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Hyperspectral imaging Accuracy Sensors Training data CASI-3 hyperspectral data subspace methods
Content Type	Text
Resource Type	Article

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