Natural Vectorization (Natvec): a novel raster to vector conversion tool ABSTRACT INTRODUCTION

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1 Natural Vectorization (Natvec): a novel raster to vector conversion tool Castilla, Guillermo, Postdoctoral Fellow Hay, Geoffrey G., Assistant Professor Chen, Gang, PhD Candidate Powers, Ryan, MSc Candidate Department of Geography, University of Calgary, 2500 University Dr. NW Calgary, AB T2N 1N4, Canada gcastill@ucalgary.ca ABSTRACT Large area thematic mapping is usually performed through the digital classification of satellite imagery. Regardless of the method selected, pixel-wise classified images typically suffer from salt and pepper noise that hinders raster to vector conversion. In order to enable a correct vectorization, classified images require a postprocessing step that removes gaps within areas covered by a predominant class. However, conventional postclassification and vectorization methods often distort boundaries, erase linear features and lead to intricate vector outlines of jagged appearance. In an effort to tackle these problems, we introduce Natvec, an 'intelligent' raster to vector conversion tool that produces an automated generalization of a raster map according to a user specified Minimum Mapping Unit (MMU). The main advantages of Natvec over currently available commercial tools are its speed, simplicity of use and natural looking vector output - that is, polygons appear similar to those digitized by a draftsperson. Natvec is intended for users interested in structures significantly larger than a pixel, who need to produce natural-looking thematic vector layers from pixel-wise classified satellite images. We briefly explain the Natvec workflow and compare its results in a sample classified image with these of a conventional tool. INTRODUCTION The integration of remote sensing (RS) data in a Geographic Information System (GIS) usually involves the conversion of raster thematic maps (i.e., classified images) to vector format, which subsequently can be overlaid and combined with other information for further analysis. Due to inherent errors in the classification and the natural variability of the mapped classes, per-pixel classified images often exhibit a lack of spatial coherency. Therefore a post-classification step is often required. There are three primary techniques that improve the spatial coherency of these images: (1) Majority analysis, where a moving window of a user-selected size is passed through the image and the center pixel in the window is replaced with the class that the majority of the pixels in the window has; (2) Clumpling, where adjacent similar classified areas are clumped together by performing morphological operations (dilate and erode) using again a moving window of a selected size; and (3) Sieving, where groups of connected pixels belonging to the same class are erased if they are smaller than an specified size. As a trade-off for the improved coherency, boundaries are often distorted, and linear features narrower than the window width are erased. In addition, conventional raster to vector tools use as vertices the corners of pixels separating the classes, which leads to convoluted and jagged polygon outlines that are difficult to simplify with automated means. The tool introduced here provides users with a solution to these problems, enabling them to derive in a single step, vector layers that can be readily used within projects requiring high cartographic standards. 1

2 Figure 1-1 Figure 1-2 Figure 1-3 Figure 1-4 Figure 1-5 Figure 1. Natvec sequence, from top to bottom: (1-1) original image; (1-2) LHI image; (1-3) watershed; (1-4) merging of adjacent classes; (1-5) annexation of small regions and vectorization NATVEC WORKFLOW Natvec source code was conceived and written by Dr Castilla in IDL (ITTVIS, 2007a), and can be run either within the commercial remote sensing software ENVI (ITTVIS, 2007b) as a user extension or stand-alone in conjunction with the freely distributed IDL Virtual Machine (IDLvm). In its most basic form, the user simply specifies the size in pixels of the Minimum Mapping Unit (MMU). Clusters of connected pixels - belonging to the same class but smaller than the MMU size - will then be incorporated into the surrounding region. Advanced usage includes further options such as (1) specifying different MMU size for each class; (2) preserving narrow corridors of some classes (e.g. rivers and roads); (3) using different rules to aggregate regions smaller than the MMU size; and (4) selecting different simplification levels for output arcs. Natvec workflow is as follows (Fig. 1). A measure of the local heterogeneity of the classified image (Fig. 1-1) is computed using a 5x5 moving window. The output image (Fig. 1-2) of this computation is converted via a watershed algorithm into a mosaic of small regions that captures the spatial variation of the original classified image (Fig. 1-3). Adjacent regions having the same prevailing class are then merged (Fig. 1-4). Then regions smaller than the MMU are aggregated. (Fig. 1-5). Finally, the result is vectorized using as vertices the centres of boundary pixels separating adjacent regions (Fig. 1-5, white outline). Local heterogeneity image (LHI) In order to create this image, a 5x5 window is centred on each pixel of the classified image, and the number of pixels within the window that belong to a different class than the one of the central pixel is used as the new Digital number (DN) of the pixel in the LHI. The histogram of this new image ranges from DN=0 (for pixels within completely homogeneous areas) to DN=24 (for isolated pixels). Pixels in the boundaries between areas with different classes will have an intermediate value. This image can be conceptualized as the Digital Elevation Model (DEM) of a virtual landscape that represents the spatial arrangement of classes in the original classified image. Thus, there will be low flat valleys (homogeneous areas) surrounded by hilly terrain (transition zones between homogeneous areas). In addition there will be some isolated peaks within the valleys (individual pixels or small clusters of pixels from a different class than the surroundings), some of them eventually corresponding to volcanoes (if the cluster is large enough so as to have some interior pixels, which would form a crater). We note that the choice of a 5x5 window was done empirically after evaluating other sizes. For example, a 3x3 window is insufficient to create a coherent LHI, while 7x7 or larger, does not significantly improve the results, cannot capture small structures, and is more time consuming. Watershed partition The watershed algorithm (Vincent and Soille 1991) finds the drainage divides, or watersheds, of the LHI virtual landscape by simulating a gradual immersion. The latter analogy consists in placing a spring in each LHI local minimum, from which pressurized underground water flows at a rate that keeps the altitude of the water plane of submersed areas the same throughout the landscape (hence the analogy with immersion). Then, in places where the water coming from two different springs would contact, we build a dam of 1-pixel thickness, slightly taller that the highest peak of the landscape. When the latter is completely submersed, we stop the immersion. The resulting network of dams defines a complete partition of the image and is deemed to capture the spatial structure of the classified image. We note that to our best of our knowledge, this is the first time that the watershed algorithm has been applied to the post-processing of RS classified images. 2

3 Region merging In this step, adjacent regions of the watershed partition that share a common prevailing class are merged. To do this, the DN of the pixels inside each region is replaced with the numeric label of the most frequent class found within each region. Next, boundary pixels (DN=0) separating regions with the same class are also assigned to that class. Annexation of regions smaller than the MMU size After the merging step, it is likely some regions exist that are still smaller than the MMU size. At present there are two alternative methods for removing them. (1) In the default method, a region smaller than the MMU is annexed to the adjacent region with which it shares most of its length. (2) In the second method, the region is annexed to the adjacent region that has the most similar class to that of the region under examination. In this case, the user has to provide a similarity distance matrix between classes. In both alternatives, the annexation sequence is ordered by size, so that the smallest regions are annexed first and the ones closest to the MMU size are annexed later. This procedure is repeated until there are no remaining regions smaller than the MMU, which typically happens after a few iterations. Note that optionally, the user can also set different MMU size for different classes. Vectorization The last step in the workflow is to convert the remaining network of boundaries into a polygon vector layer. In order to proceed, the centres of boundary pixels are considered the initial vertices forming the vector layer. This is analogous to considering boundary pixels as a transition zone that is represented by its medial axis. Nodes (junctions connecting arcs) are the centres of those boundary pixels having more than two boundary neighbours. Thus arcs correspond to chains of boundary pixels that start and end in a node. Finally, vector units (polygons) are delimited by the set of arcs bounding the corresponding region. In order to give a natural appearance to arcs, a spline interpolation can be optionally applied to the centroid of each three consecutive vertices within the arc. The arc (smoothed or not) is further simplified with a proprietary implementation of the Douglas-Peucker (1973) algorithm, which deletes redundant vertices using a user-defined tolerance whose default is equal to the pixel size. These results are then saved as a shape file (ESRI format.shp), and the associate database file (.dbf) is filled with the most frequent class found in each polygon. EXAMPLE Fig. 2-2 illustrates the results of applying Natvec to a 512x600 pixel classified image (14 classes) that can be found in the tutorial data of the ENVI distribution (filename: jsp99hym_sam ). The image (Fig.2-1) represents landcover within a 2.5x3 km 2 area, which corresponds to Stanford University's Jasper Ridge Biological Preserve, California (122.2 W, 37.4 N). In this example, the same MMU size constraint was applied to all classes, namely 40 pixels (0.1 hectare). In addition, the option to preserve corridors was set for classes 4 and 5 (pathways and roads - cyan and purple, respectively). Essentially, this option dilates 1pixel-wide linear features of the selected classes so as to retain them in the vector layer. Total processing time in a standard PC was less than 2 seconds. We note that typical processing time for a full classified Landsat scene (5000x5000 pixels) is less than 2 minutes, while a conventional tool would require tens of minutes. Fig. 2-3 illustrates the vectorization obtained through a conventional procedure, such as the one available in the standard ENVI package. The following four steps were required: (1) the classified image was filtered with a 5x5 kernel (same kernel size as Natvec) using the majority analysis tool that can be selected from the ENVI main menu bar (Classification > Post Classification > Majority/Minority Analysis); (2) regions smaller than 40 pixels were masked using the sieve tool (Classification > Post Classification > Sieve Classes); (3) the marked regions were then assigned to the most frequent surrounding class using the clumping tool (Classification > Post Classification > Clump Classes); (4) finally, the output classified image was vectorized using the pertinent tool (Classification > Post Classification > Classification to Vector). Total processing time for the four steps was approximately 10 seconds. This is five times greater than Natvec s and does not include the time invested by the human operator. A visual inspection of Figs. 2-2 and 2-3 confirms that the results of both Natvec and the conventional procedure are similar and reasonably good. However, the conventional procedure was unable to retain as separate polygons thin linear features. The lower left corner of both figures includes a smaller zoom image showing a detail (see arrow 3

4 and yellow box) of how Natvec and the ENVI sequence deal with linear features. In addition, a closer inspection (Fig. 2-4, corresponding to the red box in Figs. 2-2 and 2-3) evidences that the polygon outlines produced by Natvec (in black) are superior for cartographic purposes than the intricate and jagged ones created with the conventional procedure (in yellow). Furthermore, the size of Natvec s shape file is 160 kb, while ENVI s is 750 kb. The reason is that the latter contains 46,000 vertices, while Natvec s has only 9,000. Figure 2. Results in a 512x600 pixel image Figure 2-1 (upper left). Classified image Figure 2-2 (upper right). Natvec result Figure 2-3 (lower right). ENVI result Figure 2-4 (lower left). Detail of both vectorizations: Natvec (black); ENVI (yellow) 4

5 FINAL REMARKS In this paper we have introduced Natvec, a novel raster to vector conversion tool that efficiently produces an automated generalization of a raster map according to a reduced set of user parameters (only the MMU size in the simplest usage). Natvec rapidly generates similar but better results (in terms of delineation) than those produced by an available commercial tool, and requires considerably less computing time and user interaction. Typical processing time using a standard PC is less than 5 seconds per Mpixel. Natvec delineates polygon outlines that look professional and lack the convoluted and jagged appearance of conventional vectorizations. Natvec is a tool specially suited for users interested in structures significantly larger than a pixel, who need to produce naturallooking thematic vector layers from per-pixel classified satellite images over large areas. We aim to commercialize Natvec by 2008, and note that the tool can be tailored to specific needs, and welcome all interest. ACKNOWLEDGMENTS This research and Dr Castilla postdoctoral fellowship have been generously supported in grants to Dr Hay from the University of Calgary, the Alberta Ingenuity Fund, and the Natural Sciences and Engineering Research Council. The opinions expressed here are those of the Authors, and do not necessarily reflect the views of their funding agencies. REFERENCES Douglas, D. and T. Peucker, Algorithms for the reduction of the number of points required to represent a line or its caricature. The Canadian Cartographer, 10 (2): ITTVIS, (2007a). IDL The Data Visualization & Analysis Platform. ITTVIS, Boulder, Colorado, USA. URL: (last date accessed: 1 February 2007). ITTVIS, (2007b). ENVI - The Remote Sensing Exploitation Platform. ITTVIS, Boulder, Colorado, USA. URL: (last date accessed: 1 February 2007). Vincent, L. and P. Soille, (1991). Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (6): BIOSUMMARY Guillermo Castilla is a Spanish Forest Engineer (MSc from the Polytechnic University of Madrid, 1990) who specialised in Remote Sensing (PhD from UPM, 2003) with a fellowship from the European Space Agency (ESA, ). He works in the development of theories and methods to automate the production of geographic information from remote sensing imagery. The approach he follows involves the design of new image segmentation and classification methods that operate at several scales, based respectively on image morphology and multicriteria decision-making theory. He moved to Canada in May 2006 as a Postdoctoral Fellow, where he joined the Foothills Facility for Remote Sensing and GIScience in the Department of Geography at the University of Calgary. 5