Forest mapping accuracies are improved using a supervised nonparametric classifier with SPOT data

A.K. Skidmore, B.J. Turner

Research output: Contribution to journalArticleAcademicpeer-review

46 Citations (Scopus)
8 Downloads (Pure)

Abstract

A new supervised nonparametric classifier produces an image showing the empirical probability of correct classification for a pixel as well as a thematic image. This allows an analyst to visually locate those parts of the image where classification success can be improved. The algorithm was tested using SPOT XS data over a forest plantation in southeast Australia. The classifier produced thematic maps of higher accuracy than those from conventional supervised classifiers.
Original languageEnglish
Pages (from-to)1415-1421
JournalPhotogrammetric engineering and remote sensing
Volume54
Issue number10
Publication statusPublished - 1988

Keywords

  • ADLIB-ART-1778
  • ITC-ISI-JOURNAL-ARTICLE

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