Study On Color Scanned Topographic Map Segmentation Method

Maps have been widely used around the globe for very long time.They,especially historical topographic maps,provide a valuable and unique resource to help understand natural and human-induced changes on earth over long periods.Large number of historical maps are currently collected in archives and become available as scanned data sources.To make use of these maps in geographic information system(GIS),accurate geographic information extraction methods are urgently needed.In topographic map processing,image segmentation represents a crucial preprocessing step of which the outcome directly influences subsequent processing steps.However,it is considerably difficult to accurately extract features from topographic maps because of its poor graphical quality.The challenges are mainly in false color,blurring,noise,bleaching and color aliasing,which are caused by the bad material of paper,aging of maps,scanning misalignment,image compression process and complex geographic elements distribution.This dissertation thus engages in exploring accurate,efficient and robust methods to overcome these challenges and accomplish the segmentation task in topographic processing.The main contributions can be summarized as follows:1.We propose a topographic map segmentation method based on linear element features,to overcome the discontinuity of geographic elements during the digitization of scanned topographic maps.Linear elements are regarded as the elementary units in this method.We use linear element extraction,thinning,nodes disconnection,labeling and dilation to get the elementary units.Then the main color,which could accurately represent the color feature of linear element,is extracted for clustering on the basis of Fuzzy c-means algorithm.At last,disconnected nodes are merged into the corresponding layers to keep the continuity of the results.The experimental results show that the proposed algorithm outperforms other segmentation approaches that regarding pixels as the elementary units.2.We focus 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 method 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.3.We present a color topographic map segmentation method based on superpixel to overcome false color,mixed color and color aliasing problems occur in the raster color maps.Different from natural image,topographic map is a complex manually generated image which has amount of interlaced lines and area features.This is the reason why normal superpixel methods are not suitable for topographic map.In the proposed method,firstly,the finest partition is obtained based on double color-opponent boundary detection method and watershed approach.Then,a strict region merging method is introduced to prevent mis-merging while superpixels generated.This merging method could make the superpixel partition accurately adherent the boundary between different geographic elements.Finally,luminosity,color and texture information are combinative applied to classify the superpixel into different layers based on support vector machine.The experimental results show that the proposed method outperforms other state-of-art topographic map segmentation approaches.4.Superpixels have been widely used in lots of computer vision and image processing tasks,but rarely used in topographic map processing due to the complex spatial organization of geographic elements in this kind of images.We propose a novel superpixel generating method based on Guided Watershed Transform(GWT).Before GWT,the cues of geographic elements distribution and boundaries between different elements need to be obtained.A linear features extraction method based on compound opposite Gaussian filter and shear transform is presented to acquire the distribution information of linear features.Meanwhile,a boundary detection method which is based on the color-opponent mechanisms of the visual system is employed to get the boundary information.Then,both linear features and boundaries are input to the final partition procedure to obtain superpixels.The experiments show that our method has the best performance in shape control,size control and boundary adherence among all the comparison methods which are classic and state of the art.Furthermore,we verify the low complexity and low cost of memory in our method through experiments,which makes it possible to deal with large scale topographic maps.