Abstract
Presently, automatic classification of multispectral data images is most commonly effected on a point-by-point basis, as if the data vectors from one resolution element to the next were uncorrelated. In other words, no use is made of the spatial information contained in the scene. This is a useful but suboptimal approach, since in a practical situation strong correlations are certain to exist. "Sample classification" is a name used to refer to those classification schemes in which points a reclassified in sets (samples) rather than individually. It is assumed that all the points in a set belong to the same class. This assumption is met in applications such as crop species identification, where each field contains just one crop. Before sample classification can be applied to the fields, however, the fields themselves must be located in the data image. This is the
role of boundary finding.
LARS Tech Report Number
041773
Date of this Version
January 1973
Recommended Citation
Kettig, R. L. and Landgrebe, D. A., "Automatic Boundary and Sample Classification of Remotely Sensed Multispectral Data" (1973). LARS Technical Reports. Paper 36.
https://docs.lib.purdue.edu/larstech/36