Vector transition classes generation from fuzzy overlapping classes

Abstract

We present in this paper a way to create transition classes and to represent them with vector structures. These classes are obtained using a supervised classification algorithm based on fuzzy decision trees. This method is useful to classify data which have a space evolution following a gradient such as forest, where transitions are spread over hundreds of meter, or other natural phenomenon. The vector representation is well adapted for integration in Geographical Information Systems because it is a more flexible structure than the raster representation. The method detailed takes into account local environmental conditions and leads to non regular gradient and fuzzy structures. It allows adding classes, called transition classes, when transition areas are too spread instead of fixing an arbitrary border between classes. © 2012 Springer-Verlag.