Semi-supervised map regionalization for categorical data

Abstract

ABSTRACTThe objective of map regionalization is to group contiguous objects on a map into larger entities sharing similar properties or relationships, resulting in homogeneous regions that are easier to interpret. We propose a strategy to interactively incorporate human perception of homogeneous regions to improve unsupervised regionalization processes. The approach fits within the well-known segmentation/clustering framework. The method operates on a categorical map, introduces a contour detector for boundaries delineation with better resolution power than a regular grid tessellation to initiate a region growing process, and integrates the role of a human analyst for better classification of homogeneous areas through a semi-supervised clustering (SSC) method. This last step is achieved using pairwise clustering constraints on regions identified by the analyst on the monitor. The potential of the proposed strategy is illustrated with data extracted from the Earth Observation for the sustainable development of forests (EOSD) map of Canada. Comparisons with a recently introduced algorithm for map regionalization are provided for three different spatial scales at different steps of the method.