Classification of raster maps for automatic feature extraction

Raster maps are widely available and contain useful geographic features such as labels and road lines. To extract the geographic features, most research work relies on a manual step to first extract the foreground pixels from the maps using the distinctive colors or grayscale intensities of the pixels. This strategy requires user interaction for each map to select a set of thresholds. In this paper, we present a map classification technique that uses an image comparison feature called the luminance-boundary histogram and a nearest-neighbor classifier to identify raster maps with similar grayscale intensity usage. We can then apply previously learned thresholds to separate the foreground pixels from the raster maps that are classified in the same group instead of manually examining each map. We show that the luminance-boundary histogram achieves 95% accuracy in our map classification experiment compared to 13.33%, 86.67%, and 88.33% using three traditional image comparison features. The accurate map classification results make it possible to extract geographic features from previously unseen raster maps.