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 language | English |
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Pages (from-to) | 1415-1421 |
Journal | Photogrammetric engineering and remote sensing |
Volume | 54 |
Issue number | 10 |
Publication status | Published - 1988 |
Keywords
- ADLIB-ART-1778
- ITC-ISI-JOURNAL-ARTICLE