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.
|Number of pages||7|
|Journal||Photogrammetric Engineering and Remote Sensing|
|Publication status||Published - Oct 1988|