Using Cluster Analysis to Improve the Selection of Training Statistics in Classifying Remotely Sensed Data
A methodology for using cluster analysis to merge unsupervised classes and supervised training fields together for use in the classification process is described. The technique combines the advantages of the two claSSificatIOn approaches while minimizing the disadvantages. Discriminant analySIS IS used to test the 9uahty of the mergmg process while discrete multivariate analysis techniques are used to assess the accuracy. The results mdlcate that higher accuraCies can be achieved using this proposed approach than from supervised or unsupervised claSSificatIon alone.
Natural Resources and the Environment
Photogrammetric Engineering and Remote Sensing
American Society for Photogrammetry and Remote Sensing
Chuvieco, E. and R. Congalton. 1988. Using cluster analysis to improve the selection of training statistics in classifying remotely sensed data. Photogrammetric Engineering and Remote Sensing. Vol. 54, No. 9, pp. 1275-1281.
©1988 American Society for Photogrammetry and Remote Sensing