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

Andrew K. Skidmore*, Brian J. Turner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

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
Number of pages7
JournalPhotogrammetric Engineering and Remote Sensing
Volume54
Issue number10
Publication statusPublished - Oct 1988
Externally publishedYes

Fingerprint Dive into the research topics of 'Forest mapping accuracies are improved using a supervised nonparametric classifier with SPOT data'. Together they form a unique fingerprint.

Cite this