統計手法によるリモートセンシング画像の判別分析  [in Japanese] Contextual Segmentation of Geo-Spatial Imagery Based on Statistical Methods  [in Japanese]

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Abstract

衛星や航空機搭載のセンサにより観測された多重分光画像やレーダー画像を用いて,地表面の土地被覆を分類する統計手法について考察する.空間相関を持つ多変量正規分布の母数推定法を導出し,罰則付き尤度により空間的にカテゴリがばらつかないように判別する手法を提案する.さらに数値実験により本手法をSwitzerの方法と比較する.またカテゴリの空間配置がマルコフ確率場に従う統計モデルについて再考し,局所的な性質を調べ,実データに適応しその有効性を示す.

We consider discriminant analysis of land-cover categories based on multivariate geo-spatial data observed by artificial satellites or airborne sensors. The following contextual classification methods will be introduced through statistical treatment.<BR>First, we discuss the intrinsic model, which was introduced to take the spatial correlation of the data into account. We derive the estimation procedure for unknown parameters and their distribu-tions under normality assumption. Then, we employ a penalized likelihood principle based on the penalty due to spatial configuration such that adjacent pixels belong to different categories. We com-pare the penalized likelihood method and Switzer's smoothing method through simulation study. It is shown that our method is superior to Switzer's method and to the ordinary non-contextual method based on the linear discriminant function.<BR>Next, we assume that the categories follow a Markov random field (MRF), which is commonly used in image analysis. In this case, the MRF is based on the Mahalanobis distance for specifying the conditional distribution of the category given pixels in a neighborhood. Then, an adaptive clas-sification method based on the interactive conditional mode (ICM) algorithm is derived. We obtain the exact error rate of the classification of the center pixel given pixels in a local window, and it is shown that the ICM algorithm reduces the error rate in most cases. Finally, our adaptive ICM method is applied to the real data set provided by IEEE Geoscience and Remote Sensing Society for benchmark of classifications. We examine several models for the class-conditional densities and our contextual classification result shows the best performance.

Journal

  • Ouyou toukeigaku

    Ouyou toukeigaku 31(1), 3-21, 2002-07-31

    Japanese Society of Applied Statistics

References:  23

Cited by:  1

Codes

  • NII Article ID (NAID)
    10009669479
  • NII NACSIS-CAT ID (NCID)
    AN00330942
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    02850370
  • NDL Article ID
    6252155
  • NDL Source Classification
    ZM31(科学技術--数学)
  • NDL Call No.
    Z15-401
  • Data Source
    CJP  CJPref  NDL  J-STAGE 
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