Engineering Commons^™

Neural Networks Versus Nonparametric Neighbor-Based Classifiers For Semisupervised Classification Of Landsat Thematic Mapper Imagery, Perry J. Hardin

Faculty Publications

Semisupervised classification is one approach to converting multiband optical and infrared imagery into landcover maps. First, a sample of image pixels is extracted and clustered into several classes. The analyst next combines the clusters by hand to create a smaller set of groups that correspond to a useful landcover classification. The remaining image pixels are then assigned to one of the aggregated cluster groups by use of a per-pixel classifier. Since the cluster aggregation process frequently creates groups with multivariate shapes ill suited for parametric classifiers, there has been renewed interest in nonparametric methods for the task. This research reports …