Indexed on: 16 Jan '09Published on: 16 Jan '09Published in: GeoInformatica
A novel approach to color image segmentation (CIS) in scanned archival topographic maps of the 19th century is presented. Archival maps provide unique information for GIS-based change detection and are the only spatially contiguous data sources prior to the establishment of remote sensing. Processing such documents is challenging due to their very low graphical quality caused by ageing, manual production and scanning. Typical artifacts are high degrees of mixed and false coloring, as well as blurring in the images. Existing approaches for segmentation in cartographic documents are normally presented using well-conditioned maps. The CIS approach presented here uses information from the local image plane, the frequency domain and color space. As a first step, iterative clustering is based on local homogeneity, frequency of homogeneity-tested pixels and similarity. By defining a peak-finding rule, “hidden” color layer prototypes can be identified without prior knowledge. Based on these prototypes a constrained seeded region growing (SRG) process is carried out to find connected regions of color layers using color similarity and spatial connectivity. The method was tested on map pages with different graphical properties with robust results as derived from an accuracy assessment.