Refining image segmentation by polygon skeletonization

Abstract

A skeletonization algorithm was encoded and applied to a test data set of land-use polygons taken from a USGS digital land use dataset at 1:250,000. The distance transform produced by this method was instrumental in the description of the shape, size, and level of generalization of the outlines of the polygons. A comparison of the topology of skeletons for forested wetlands and lakes indicated that some distinction based solely upon the shape properties of the areas is possible, and may be of use in an intelligent automated land cover classification system.