Technical note non-parametric test of overlap in multispectral classification

A. K. Skidmore, G. W. Forbes, D. J. Carpenter

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Many strategies have been developed for the allocation of image pixels, or picture elements, to classes in order to obtain segmentation of images. Difficulties are encountered when a pixel may be assigned to more than one class according to the information gleaned from training sets. The following question arises: is there real overlap or are the training sets spectrally discrete? A general algorithm is presented which quantifies the degree of overlap of classes defined by training sets drawn from an image. Test results show that age classes within a forest plantation are spectrally discrete, even though poor classification accuracies were obtained using conventional classifiers.

Original languageEnglish
Pages (from-to)777-785
Number of pages9
JournalInternational Journal of Remote Sensing
Volume9
Issue number4
DOIs
Publication statusPublished - 1988
Externally publishedYes

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