Contextual unsupervised classification of remotely sensed imagery with mixels

Shuji Kawaguchi, Ryuei Nishii

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

We propose a contextual unsupervised classification method of geostatistical data based on combination of Ward clustering method and Markov random fields (MRF). Image is clustered into classes by using not only spectrum of pixels but also spatial information. For the classification of remote sensing data of low spatial resolution, the treatment of mixed pixel is importance. From the knowledge that the most of mixed pixels locate in boundaries of land-covers, we first detect edge pixels and remove them from the image. We here introduce a new measure of spatial adjacency of the classes. Spatial adjacency is used to MRF-based update of the classes. Clustering of edge pixels are processed as final step. It is shown that the proposed method gives higher accuracy than conventional clustering method does.

本文言語英語
ホスト出版物のタイトルImage and Signal Processing for Remote Sensing XII
DOI
出版ステータス出版済み - 12 1 2006
イベントImage and Signal Processing for Remote Sensing XII - Stockholm, スウェーデン
継続期間: 9 11 20069 14 2006

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
6365
ISSN(印刷版)0277-786X

その他

その他Image and Signal Processing for Remote Sensing XII
Countryスウェーデン
CityStockholm
Period9/11/069/14/06

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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