Feature extraction using pixel-level and object-level analysis

In a graphical information system (GIS), a method for processing a digital image depicting one or more physical objects to locate a feature in the digital image, the method comprising the following:

• by a processor, performing a pixel-level cue algorithm on a digital raster image, the raster image depicting the one or more physical objects by a set of pixels;
• based on the results of the pixel-level cue algorithm, generating a pixel probability layer in which each pixel's value represents a probability that it is the feature of interest;
• converting the pixel probability layer into a raster object layer which contains pixels that are grouped as raster objects;
• for each raster object, calculating a mean probability of all of the probability pixels corresponding to each raster object;
• converting at least a portion of the raster object layer to a digital vector layer by geometric modeling, wherein geometric modeling uses geometric primitive including points, lines, curves and polygons to generate a representation of digital vector objects represented by the points, lines, curves and polygons, and wherein each digital vector layer object is associated with the mean probability calculated from the raster object from which the vector object was converted;
• performing an object-level cue algorithm on the digital vector layer representation of the one or more digital vector layer objects;
• and
• identifying a feature using a result of the pixel-level cue algorithm and a result of the object-level cue algorithm performed.