A contour-line color layer separation algorithm based on fuzzy clustering and region growing

Abstract

The color layers of contour-lines separated from scanned topographic map are the basis of contour-line extraction, but it is difficult to separate them well due to the color aliasing and mixed color problems. This paper will focus us on contour-line color layer separation and presents a novel approach for it based on fuzzy clustering and Single-prototype Region Growing for Contour-line Layer (SRGCL). The purpose of this paper is to provide a solution for processing scanned topographic maps on which contour-lines are abundant and densely distributed, for example, in the condition similar to hilly areas and mountainous regions, the contour-lines always occupy the largest proportion in linear features and the contour-line separation is the most difficult task. The proposed approach includes steps as follows. First step, line features are extracted from the map to reduce the interference from area features in fuzzy clustering. Second step, fuzzy clustering algorithm is employed to obtain membership matrix of pixels in the line map. Third step, based on the membership matrix, we obtain the most-similar prototype and the second-similar prototype of each pixel as the indicators of the pixel in SRGCL. The spatial relationship and the fuzzy similarity of color features are used in SRGCL to overcome the inaccurate classification of ambiguous pixels. The procedure focusing on single contour-line layer will improve the accuracy of contour-line segmentation result of SRGCL relative to general segmentation methods. We verified the algorithm on several USGS historical maps, the experimental results show that our algorithm produces contour-line color layers with good continuity and few noises, which verifies the improvement in contour-line color layer separation of our algorithm relative to two general segmentation methods.