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Classification of CASI-3 hyperspectral image by subspace method
http://hdl.handle.net/10659/2970
http://hdl.handle.net/10659/29707c335bcf-3e6c-4024-9f04-7af34339aa7c
名前 / ファイル | ライセンス | アクション |
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S-2012-11_hoshino.pdf (401.6 kB)
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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公開日 | 2013-06-04 | |||||
タイトル | ||||||
タイトル | Classification of CASI-3 hyperspectral image by subspace method | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | CASI-3 | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | hyperspectral data | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | subspace methods | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
資源タイプ | conference paper | |||||
著者 |
Hoshino, B.
× Hoshino, B.× Bagan, H.× Nakazawa, A.× Kaneko, M.× Kawai, M.× Yabuki, T. |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | 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-Loeve 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. | |||||
書誌情報 |
2011 IEEE International Geoscience and Remote Sensing Symposium p. 724-727, 発行日 2011-07 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 2153-6996 | |||||
ISBN | ||||||
識別子タイプ | ISBN | |||||
関連識別子 | 9781457710032 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1109/IGARSS.2011.6049232 | |||||
権利 | ||||||
権利情報 | ©2011 IEEE | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
出版者 | ||||||
出版者 | IEEE | |||||
資源タイプ | ||||||
内容記述タイプ | Other | |||||
内容記述 | Article |