Towards the integration of GIS and remote-sensing image analysis techniques

Classical image analysis techniques have proven to be powerful tools in various remote-sensing image interpretation problems. However, applied to large images their usefulness is limited as the spatial complexity of classes used in land-cover databases often exceeds the identification capability of the methods. Moreover, atmospheric and soil conditions introduce a substantial within-class variability. Land-cover/land-use databases can contain 40 or more different categories which cannot all be derived directly from the image data. The robust integration of GIS and remote sensing image interpretation techniques is important, but is feasible only when both possibilities and limitations are considered. In this paper, the design and implementation is described of a tool for the updating of land-cover polygons by remote-sensing imagery. After a preliminary analysis of the neighbouring polygons (i.e., background) around a polygon to update (i.e., object), the best feature is selected out of a set of more than 30 features based on its ability to separate object from background. This best feature is used in a successive image-labelling step. The labelling step adopted in this paper is based on a fuzzy intensity connectedness measure.