改良部分空間法に基づくハイパースペクトラルデータのカテゴリ分解 A method for category decomposition on learning subspace method

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著者

    • 陳, 華慧 チェン, ホァフェイ

書誌事項

タイトル

改良部分空間法に基づくハイパースペクトラルデータのカテゴリ分解

タイトル別名

A method for category decomposition on learning subspace method

著者名

陳, 華慧

著者別名

チェン, ホァフェイ

学位授与大学

佐賀大学

取得学位

博士 (学術)

学位授与番号

甲第330号

学位授与年月日

2007-03-23

注記・抄録

博士論文

An unmixing method for hyperspectral data based on subspace method with learning process is proposed. Unmixing methods can be divided into three categories, inversion method, SVD (Singular Value Decomposition) based method, and subspace method. Although these methods works well if the distribution of the hyperspectral data in feature space can be represented as somewhat convex function, it is now allways true. Unmixing method proposed here does work even if the distribution is expressed with concave function because the method adjust the axis of subspace through a learning process. Through experimental studies with AVIRIS (Airborne Visible InfraRed Imaging Spectrometer) data, it is found that the proposed method achieves 16.3% of improvement of the unmixing accuracy in terms of root mean square error in comparison to the well known least square unmixing method. Also it is foundthat the proposed method shows 15.0% better accuracy in comparison to the subspace based unmixing method. The reasons for the improvement are clarified in a comprehensive manner with the simple example of the 3D feature space together with two categories.

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各種コード

  • NII論文ID(NAID)
    500000370541
  • NII著者ID(NRID)
    • 8000000371700
  • 本文言語コード
    • jpn
  • NDL書誌ID
    • 000008525652
  • データ提供元
    • 機関リポジトリ
    • NDL ONLINE
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