Improved Contextual Classifiers of Multispectral Image Data

• WATANABE, Takashi

• SUZUKI, Hitoshi

• TANBA, Sumio

• YOKOYAMA, Ryuzo

抄録

Contextual classification of multispectral image data in remote sensing is discussed and concretely two improved contextual classifiers are proposed. The first is the extended adaptive classifier which partitions an image successively into homogeneously distributed square regions and applies a collective classification decision to each region. The second is the accelerated probabilistic relaxation which updates a classification result fast by adopting a pixelwise stopping rule. The evaluation experiment with a pseudo LANDSAT multispectral image shows that the proposed methods give higher classification accuracies than the compound decision method known as a standard contextual classifier.

収録刊行物

• IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences

  IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences E77-A (9), 1445-1450, 1994-09-01

  The Institute of Electronics, Information and Communication Engineers

キーワード

• contextual classification
• multispectral image
• remote sensing
• probabilistic relaxation

詳細情報

• CRID: 1050282677654742400
• NII論文ID: 110003215894
• NII書誌ID: AA10826239
• ISSN: 09168508
• Web Site: https://iwate-u.repo.nii.ac.jp/records/10059
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles