A Semi-Automated Geological Model from Remotely Sensed Data for GIS Mapping and Analysis.

The option to introduce spaceborne data that enables to recognize spatial patterns, to enhance ground-collected data, to acquire high-resolution spatial and spectral data, and to incorporate this data into a Geographic Information Systems (GIS), has been recently recognized and used by the geological research community. The current paper presents the development of a semi-automated model for geological mapping and 3D geodata investigation based on a set of image processing and GIS techniques. The aim of the developed methodology is to extract lithological and structural information by combining geological object recognition of hyperspectral spaceborne data and GIS data mining and analysis. Input data consist of a Digital Terrain Model (DTM) and a hyperspectral satellite image. These data serve as the basis for a supervised classification of the geological units and for extracting geostructural data in order to construct a 3D database of the area's geology. The output is stratigraphic information (such as dip and strike) that can be used for mapping and constructing 2.5D/3D models of the subsurface or, in conjunction with additional thematic layers, for geological spatial analysis.