filtering and segmentation of digitized land use map images

IJDAR (1998) 1: 167–174InternationalJournal on IJDARDocument Analysis and Recognitionc© Springer-Verlag 1998

Filtering and segmentation of digitized land use map images

Rafael Santos1, Takeshi Ohashi2, Takaichi Yoshida2, Toshiaki Ejima2

1 Instituto de Pesquisa e Desenvolvimento, Universidade do Vale do Paraıba, Av. Shishima Hifumi 2911, Urbanova, Sao Josedos Campos, Sao Paulo 12244-000, Brazil; e-mail: [email protected]

2 Department of Artificial Intelligence, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology,Kawazu 680-4, Iizuka, Fukuoka 820-8502, Japan; e-mail: {ohashi,takaichi,toshi}@mickey.ai.kyutech.ac.jp

Received: October 1, 1997 / Revised June 16, 1998

Abstract. One important step in the analysis of digi-tized land use map images is the separation of the infor-mation in layers. In this paper we present a techniquecalled Selective Attention Filter which is able to extractor enhance some features of the image that correspond toconceptual layers in the map by extracting informationfrom results of clustering of local regions on the map.Different parameters can be used to extract or enhancedifferent information on the image. Details on the algo-rithm, examples of application of the filter and resultsare also presented.

Key words: Map image processing – Segmentation –Land use maps – Clustering – Filtering

1 Introduction

Maps are composed by layers which contain different in-formation about the region covered by the map, over-layed so a user can identify both the original layer infor-mation and its relation with the information contained inother layers. In order to automatically or semi-automati-cally extract information from the maps that can be usedby map analysis systems, a map image processing sys-tem must also be able to identify and isolate the differentlayers present in a digital map image [1].

Identification or segmentation of points, lines and re-gions in digital map images is a task that must be doneby considering at least the spectral and spatial infor-mation present in those maps, and often context infor-mation is required to achieve a better result. Examplesof spectral information are simple color or gray level ofthe pixel on the digitized map, which can be used todeterminate the class which that pixel belongs to. Spa-tial information is usually associated with features suchas continuity and homogeneity, and is very importantfor the detection of roads and linear elements. Contextis the most complex feature that can be used for seg-mentation, and consists of high-level descriptions of the

Correspondence to: R. Santos

relations between different elements on the digitized mapimage or information obtained from the map index tableor legend.

For most simple layer segmentation tasks, the spec-tral and spatial information suffice. There are techniquesthat use color and neighborhood information to segmenttext and lines [2], line and symbol features obtained fromthe map legend for contextual lines identification [3], hi-erarchical image models based on chain codes where thebasic entities are lines [4], etc. All those approaches areable to extract one or more layers from the original mapimage.

Segmentation can become complicated when the in-formation present in one layer interferes with the detec-tion of features or elements in another layer. One ex-ample is with vegetation land use maps, which have thecommon layers present in simpler land use maps, such astransportation networks, urban regions, marks, symbols,isolines, and where the land use category is representedby a background color and in some cases with foregroundcolor marks over the background. An example of a landuse map with detailed vegetation information is shownin Fig 6. One common task in using land use maps isto determine the area of a category or vegetation class,which requires the identification of the region with thesame background/foreground combination. In this casethe information in other layers can be considered as in-terfering information or noise, since the overlay of theselayers changes the features of the background color.

In this paper we present a technique to extract fea-tures from color land use maps which is able to reducethe interfering elements while preserving the edges of re-gions with different colors, and can also enhance otherfeatures in the image. First, in Sect. 2 we present a modelfor land use map images which will lead to the choice ofalgorithms and parameters, then in Sect. 3 we presentthe Selective Attention Filter technique, in comparisonwith other filters, and in Sect. 4 some experiments andresults are shown. Section 5 presents conclusions, re-marks and plans for future work.

168 R. Santos et al.: Filtering and segmentation of digitized land use map images

Fig. 1. A model of layers in a land use map

2 A model for the land use map

In the introduction we commented on how a map is com-posed of different layers with different kinds of informa-tion on it. The division of the map information in layerswill depend on the point of view of the user, the analy-sis task and/or the map analysis system, and does notneed to be unique for a map. One model for the layersin a land use map where layers correspond to differentcategories of information is shown in Fig. 1.

For the processing of the land use maps in our ap-proach we will consider only four layers. These layers andsome information that can be used to determine whethera pixel belongs or not to the layers are: the BackgroundColor, which is the principal information for identifica-tion of regions of similarity, will correspond to the ma-jority of pixels in a specific region; the Foreground Color,which is used to discriminate different classes with thesame background color, will have a different color thanthe background and be fewer in number; the Isolinesand Marks, which are elements present on other layersof the map which are considered interfering elements,will be darker than the background color and also fewerin number; and the Numbers and Borders, which helpidentify the land use categories and borders between dif-ferent land use regions, will be considerably darker thanthe background color and fewer in number.

The background and foreground colors in the modelabove correspond to the land use layer in the Fig. 1. Allother layers shown in Fig. 1 will be represented eitherby isolines and marks or numbers and borders, since forour task there is no need to discriminate between isolinesand roads, etc.

The choice of a particular model for the layers is doneto simplify the selection of algorithms and parameters forextraction of the layers’ information. In our example, thechoice of this model is very convenient because all the

layers can be identified by a combination of spectral andspatial features as shown above, with spectral informa-tion conveyed by relative brightness or color differenceand spatial information conveyed by the number of sim-ilar pixels in a region. The algorithm for segmentation ofthe image can be determined by these measurable fea-tures in regions of the image.

This model for the land use map images is not perfectsince some of the foreground markers on the maps havethe same spectral and spatial characteristics of the bor-ders, but in our application it will not matter since we’llfocus on the extraction of the background and if nec-essary identify regions with the same background anddifferent foreground.

3 The Selective Attention Filter

In this section we present a technique to extract thebackground color for the first layer and enhance the fore-ground points for the second layer, called Selective At-tention Filter (SAF) [5], which will be defined and ex-plained in detail.

In our model for the layers in the map image the as-signment of pixels to layers was specified by parameterssuch as the count of similar pixels in a region and thecolor of those pixels. For the background of the pixel es-pecially, a small region around it must be considered. Un-fortunately, any small region in the digitized map imagewill contain not only the background pixels, but also in-terfering elements, foreground pixels, and probably pix-els of the background of neighbor regions. These pixelsbelonging to other classes must be filtered or ignored.

Instead of considering all pixels of a region in order toextract the desired information, the Selective AttentionFilter first clusters the data and then selects a cluster(called the attention cluster) based on the model for thelayers which will represent the feature we want to extractor enhance. The final value for the output pixel for thefiltering or enhancement process will be calculated fromthe features extracted from the attention cluster. Thenext section will present the general description of theSAF with details on the algorithm steps and parameters.

3.1 General description of the SAF

The Selective Attention Filter can be described in a moregeneral form as an operator O over images or data setsof the form

O = 〈N, C, S, F 〉where N is a neighborhood operator which specifies theneighborhood (size, shape) for the filter, C is a clus-tering algorithm, which specifies the clustering method,metric and similarity measure used on it for clusteringof all points in the neighborhood, S is a selection heuris-tic, which will determine which clusters will be used forselection of the attention cluster and the attention clus-ter itself, and F is a feature to be extracted from the

R. Santos et al.: Filtering and segmentation of digitized land use map images 169

ClusteringAlgorithm C

Neighborhood N

Feature F

SelectionHeuristic S

AttentionCluster

Input image

Output image

(extracted fromAttention Cluster)

Fig. 2. The Selective Attention Filter algorithm

points in the selected cluster (mode, average, number ofassigned elements, etc). The Selective Attention Filteralgorithm is illustrated in Fig. 2.

In Fig. 2, a 5 × 5 square neighborhood N is usedaround all pixels in the input image, creating a set of25 pixels which will be clustered by the algorithm C in6 clusters. From those clusters, the selection heuristic Swill extract the attention cluster, from which a feature Fwill be extracted and assigned to the output image. Thenumber of dimensions on the input and output imagesdoes not need to be the same, depending on the chosenfeature.

3.2 Choice of parameters for the SAF

Some details for the choice of the parameters for theSAF (considered for the software implementation and asgeneral guidelines) are:– The neighborhood operator N is usually a square

even-sided neighborhood that will be extracted fromaround all pixels being considered. Circular neigh-borhoods could also be used but would increase theprocessing time.

– The clustering algorithm C is the K-Means cluster-ing algorithm [6,7], chosen for simplicity and speed.Other clustering algorithms could also be used. Thenumber of clusters should be large enough to ensurethat the interfering elements and pixels of interestwill fall on different clusters, but small enough toavoid splitting the cluster with pixels of interest. Formost enhancing or filtering tasks, 3–5 clusters willsuffice.

– The selection heuristic S can be divided in two sub-steps: first, an optional pre-elimination of clusterswhich aren’t related to the cluster of interest is doneand repeated if necessary. This action ensures thata false candidate to the attention cluster won’t bechosen. The second sub-step is the selection of theattention cluster itself, which defines the filtering orenhancing operation, and is defined by the model forthe image described in Sect. 2.

– The feature F is another important parameter. Forthe processing of land use map images we are using

only the center of the cluster (mean of the clustervalues), but other features could be extracted by us-ing the variance of the cluster or even the number ofpixels assigned to that cluster.

The parameters N, C, S and F are usually estimatedby considering the image model and characteristics andpurpose of the filter. Two examples for the Selective At-tention Filter with the parameters used are:

– Extraction of the background color. According to ourmap image model, the background color of the re-gion is related with the cluster with more membersassigned to it. The region must be large enough soenough background pixels will be represented.For the land use map image, the filtered backgroundcolor can be classified to create mutually exclusiveland use regions. For this task, good results are ob-tained with regions 9 × 9–15 × 15 around the centralpixel, clustered in 3–5 clusters, with the darkest clus-ter (which center is closest to the coordinate 0, 0, 0 inthe RGB space) eliminated, the attention cluster be-ing the cluster with more elements assigned to it andthe feature being the center of the attention cluster.A result of the application of this filter is shown inFig. 7.

– Enhancement of the foreground color and marks. Ac-cording to our map image model, the foregroundcolor is always darker than the background color, re-gardless of the number of pixels on it. Regions shouldbe small to avoid excessive dilation of the dark areas.The foreground color and marks could be enhancedto increase the contrast of the image or to facilitatetheir isolation or extraction from the map image, al-lowing the separation of regions whose backgroundcolor is similar but have different foreground coloredmarks on it. For this task, good results are obtainedwith regions 3 × 3 or 5 × 5 around the central pixel,clustered in 3–5 clusters, the attention cluster beingthe darkest cluster with more elements assigned to itand the feature being the center of the attention clus-ter. A result of the application of this filter is shownin Fig. 8.It must be pointed that for simple enhancement ofelements darker than the foreground, a simple mor-phological erosion applied to each RGB band sepa-rately will be faster and yield better contrast in theresults but will perform poorly in the presence of pep-per noise, while the SAF can be tuned to eliminatesmall dark clusters that correspond to pepper noise.The SAF has the advantage of being able to identifyclusters close to any specific point in the parameterspace, and could be used to enhance marks of a spe-cific color.

The notation O = 〈N, C, S, F 〉 could be used for rep-resenting other spatial filters: for example, for the av-erage smoothing filter C is a clustering operator whichsimply reproduces all data in the neighborhood N , theselection heuristic S selects that single cluster and thefeature F is the average of the values on the cluster. Inthe next section we will compare the expected perfor-


Fig. 3. Sample region

0

1

2

3

4

5

6

0 50 100 150 200 250

AvgMedMode

Fig. 4. Average, median, mode values for sample region

0

1

2

3

4

5

6

0 50 100 150 200 250

SAFCluster 1Cluster 2Cluster 3

Fig. 5. SAF value and clusters for sample region

mance of the SAF when compared with other commonlyused spatial filters.

3.3 Comparison of the Selective Attention Filterwith other spatial filters

Several commonly used filters with a simpler implemen-tation could be used instead of the Selective AttentionFilter for extraction or enhancing of features, but forimages with interfering elements all of them have somedisadvantages. In this section we examine some proper-ties of some common spatial filters and compare themwith the Selective Attention Filter for the backgroundextraction and foreground enhancement tasks.

– Average (smooth) filter: this filter would smooththe region around a pixel, reducing the influence of

outliers when those are few compared with the back-ground, but would also blur edges, specially edgesbetween regions of colors which are spectrally differ-ent. It does not preserve edges.The SAF preserve edges to some extent by eliminat-ing outliers by clustering and extracting the averageof the pixels in the attention cluster.

– Median and rank filters: the median and rank fil-ters also smooth regions while preserving edges tosome extent, but will not always correspond to themajority of the pixels in a region. The weighted me-dian filter [8] could yield better results if weightscould be selected to match the expected majority ofpixels.The SAF yields a better estimation of the majority ofthe pixels by selecting the cluster with more membersas the attention cluster.

– Mode filter: the mode filter will be ideally the bestchoice for extraction of the background color underthe assumption that the background color will cor-respond to the majority of the pixels in a region.The main problem with the use of the mode filter isthat when the regions are small, the distribution ofthe values will be very sparse, and the mode valuein this distribution could be wrongly estimated sinceusually there wouldn’t be a value significantly morefrequent than others.The SAF will give a good estimate for the most fre-quent value by the selection of the cluster with mostpixels.

– K-nearest neighbor average filter: this filter issimilar to the median filter. It works by selecting theK-nearest spectral neighbors from a region around apixel and calculating the average of these K values[9], considering as outliers the pixels which are notin the set of the K-nearest neighbors. This filter alsopreserve edges to some extent but its results are verydependent on the central pixel value.The SAF is not dependent on the central pixel, theoutliers are eliminated by the selection process.

– Mathematical morphology operations: twogray-level basic morphological operations can also beused to enhance features in the image. The Dila-tion operator corresponds to the maximum rank fil-ter and can be used to enhance the bright regions inan image, shrinking the interfering elements regions ifthose are darker than the background. The Erosionoperator corresponds to the minimum rank filter andcan be used to enhance dark regions.The SAF can select which feature will be enhanced,being able to extract the background whether it isdarker or lighter than the foreground.

The Selective Attention Filter is more flexible thanthe approaches shown above in respect to the possibilityof elimination of values that are not related with the fea-ture we want to extract or enhance. Generically speak-ing, for extraction of the background the SAF combinesfeatures of the median filter by eliminating outliers andthe average filter by using the average of the values inthe selected cluster.


To show a visual comparison of the results of theaverage, median, mode and SAF filter, those three oper-ators were applied over a small (9 × 9) gray-level regionof a map image (shown enlarged in Fig. 3). The regioncontains two different backgrounds, isoline, and borderpixels.

Figure 4 shows the histogram for the gray values inthe region in Fig. 3 with the median, average and modevalues for the pixels marked in the histogram. Figure 5shows the same histogram with the image clustered inthree clusters, with the darkest cluster eliminated andthe attention cluster being chosen from the remainingclusters as the one with more pixels assigned to it. Theattention cluster is the cluster in the middle and its av-erage value is 132. The average, median and mode valueswere calculated as 116, 102 and 90 respectively. For thatregion the estimated background gray level (calculatedas the average of pixels with the same background butwithout interfering elements on it) was 138.

4 Experiments and results

Some results of experiments with the SAF are shown inthe color plates. Figure 6 shows a region of a digitizedland map image of Takamori, Japan, with several landuse classes shown in different colors with overlays of in-terfering elements (isolines, marks, characters). The im-age size is 150 × 300 pixels, and corresponds to a smallregion of a whole map sheet digitized at 200 dots-per-inch.

The image in Fig. 6 was processed with the SAF toextract the background color. The SAF parameters were:cluster the points around N = a 9 × 9 region using C= the K-means algorithm with 3 clusters, applying theheuristics S = 1) ignore the cluster whose center is clos-est to the RGB coordinate (0, 0, 0) and 2) consider theattention cluster as the remaining cluster with more pix-els assigned to it. The feature F is the center of the at-tention cluster. The result of the filtering is shown inFig. 7, where it can be seen that most of the elementsconsidered as interfering or noise were eliminated.

The same image was processed with SAF to enhancethe foreground elements under the assumption that theyare darker than the background (according to our imagemodel), using N = 3×3 neighborhood, C = the K-meansalgorithm with 3 clusters, S = selection of the attentioncluster being the darkest cluster, and F as the center ofthe attention cluster. The result is shown in Fig. 8.

The background color feature image was classifiedwith a K-nearest neighbors algorithm with samples forall classes extracted from the image. The classificationresult is shown in Fig. 9 (the colors for the classifica-tion results were chosen to be similar to the perceivedcolors in the original map). For visual comparison, wealso applied the median and average filters (both with a9× 9 filtering element) to the image in Fig. 6 and classi-fied those images with the same parameters we used forthe classification of the SAF-filtered image. Results areshown in Fig. 10 for the median filtered image and inFig. 11 for the average filtered image. The result of the

SAF image has less misclassification on the borders be-tween classes and inside the region of classes 34 and 64,although some regions are still misclassified since the fil-ter neighborhood wasn’t large enough to erase the inter-fering elements in those regions.

5 Conclusions and remarks

In this paper we proposed and explained in detail theSelective Attention Filter, which can be used to filter orenhance features in noisy images or images with inter-fering elements. The SAF parameters can be adjusted towork with different types of images and features, beingless sensitive to noise when compared with other com-monly used filters.

For some experiments with the classification of thebackground color of land use digitized map images,where there are interfering elements which can be con-sidered noise, pre-filtering with the SAF yielded betterresults than the results obtained with other common fil-ters (There isn’t a “ground truth” image that can beused to calculate the percentage of correctly classifiedpixels for that area.) In the classification results for theSAF-filtered image, there are still some misclassified re-gions where there were a dense concentration of isolines,border lines and/or foreground marks. In these regions,the heuristics for selection of the attention cluster (whichshould correspond to the background color) fail, since themajority of the pixels in the region would correspond toimage elements other than the background color. Largerneighborhoods could be used, but that could result inloss of precision in thin regions, so this remains a prob-lem to be solved.

The final classification results can be used to estimatethe land use areas for different classes in the land usemap. From the classified result, a simple raster-to-vectorapproach could be used to extract the regions’ polygonsfor entry in a GIS system, for the creation of a land useinformation layer.

In Sect. 3.2 we commented on how the SAF param-eters could be chosen to enhance foreground marks forthe separation of regions whose background is similarbut have different colored foreground marks. In the mapsegmentation application described in this paper, theclasses 33 and 34 have similar background but differ-ent foreground marks, but since they represent almostthe same class (variations of konara bushes) they can beclassified as being the same class. If necessary, an addi-tional classification step which considers the presence ornot of foreground marks could be used to discriminatebetween the two classes.

The foreground color enhancing approach could bedone to increase the contrast of any image with any num-ber of bands without having the side effects of operationssuch as the mathematical morphology erosion.

The process of extraction of the background colorcan be generalized to extraction of a generic feature ofa region by clustering the feature values. We are inves-tigating the use of the SAF to extract the generic di-


rectionality of textured regions with interfering elementsand extraction of other texture measures.

A disadvantage of the SAF filter is that it is slowsince clustering must be done for all pixels (or smallregions) in an image. A software prototype of the fil-ter, implemented using the Khoros [10] system libraries(which added a data representation overhead) averaged16 pixels per second in a Pentium 90 Mhz personal com-puter, which can be prohibitive for large images. Threeapproaches are being considered to speed up the SAF:use of improved libraries, use of a running clustering ap-proach (where the cluster centers are reused from previ-ous clusterings), and use of a so-called “clustering map”which is a basic texture measure on the image that canbe used to identify regions where clustering is not neces-sary (e.g., smooth regions) and using the simple averageinstead of the SAF for those regions.

The clustering done within the SAF filter is the sim-ple K-Means with the Euclidean metric and we will con-sider the Conditional Fuzzy C-Means approach [11]where the weights for each cluster will be obtained fromsimple measures in the image.

We are also investigating other possible uses of theSAF, specifically, for filtering samples for classes in Land-sat TM-5 multispectral satellite images, to eliminate out-liers in samples for classes to improve classification re-sults. It is expected that the SAF could be used withany kind of multispectral images to eliminate noise orenhance features provided a simple model can be usedto specify the SAF parameters.

Acknowledgements. Rafael Santos was supported by Univap -Paraıba Valley University, Brazil and the Japanese Ministryof Education. The maps used in the examples were kindlyobtained from N.S. Environmental Science Consultants, Co.Ltd., Japan. The original maps are copyright c© The JapaneseGovernment Environment Agency. The authors would like tothank the anonymous reviewers for several suggestions onthis paper.

References

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3. R. Kasturi, J. Alemany. (1988) Information Extractionfrom Images of Paper-Based Maps. IEEE Transact. Soft-ware Eng 14(5): 671–675

4. H. V. Jagadish, L. O’Gorman. (1989) An object modelfor image recognition. IEEE Computer, December 1989,33–41

5. R. Santos, T. Ohashi, T. Yoshida, T. Ejima. (1997) Se-lective Attention Filtering of digitized map images. Proc.Khoros Symposium ’97, 16–25

6. R. O. Duda, P. E. Hart. (1973) Pattern Classificationand Scene Analysis. John Wiley and Sons

7. C. G. Looney. (1997) Pattern Recognition using NeuralNetworks. Oxford University Press

8. I. Pitas. (1993) Digital Image Processing Algorithms.Englewood Cliffs, NJ: Prentice Hall

9. D. W. R. Paulus, J. Hornegger. (1995) Pattern Recog-nition and Image Processing in C++. Verlag Vieweg

10. Khoral Research Inc. http://www.khoral.com11. W. Pedrycz. (1996) Conditional Fuzzy C-Means. Pat-

tern Recog Lett 17: 625–631


6 Additional figures

Fig. 6. Land use map (vegetation) Fig. 7. Extraction of the back-ground color with SAF

Fig. 8. Enhancement of markersand lines with SAF

Fig. 9. Classified image (SAF) Fig. 10. Classified image (median) Fig. 11. Classified image (average)


Rafael Santos received his BSdegree in computer science fromthe Federal University of ParaState in Brazil in 1990, and hismaster’s and PhD degree in com-puter science at the Kyushu In-stitute of Technology in 1995and 1998, respectively. His ar-eas of interest are computer vi-sion and image processing ap-plications and pattern classifica-tion. He is with the Paraıba Val-ley University in Brazil and is amember of IEEE and SPIE.

Takeshi Ohashi received hisBE and ME degrees from Na-gaoka University of Technology,Nagaoka, Japan in 1989 and1991, respectively. From April1991 he has been working atthe Department of ArtificialIntelligence, Kyushu Instituteof Technology as a researchassociate. His research interestsinclude image processing, pat-tern recognition, robot visionand human-computer interac-tion. He is a member of IPSJ,

JSSST and IEEE.

Takaichi Yoshida received hisBS degree in electrical engineer-ing from Keio University, Japanin 1982 and the MS and PhD de-grees in computer science fromKeio University, Japan in 1984and 1987 respectively. He is anassociate professor of computerscience in the Department of Ar-tificial Intelligence, Kyushu In-stitute of Technology. One ofhis research interests is in geo-graphic information systems onobject-oriented database man-

agement systems. He is a member of ACM, IEEE, JapanSociety for Software Science and Technology, Japan Societyfor Artificial Intelligence and Information Processing Societyof Japan.

Toshiaki Ejima received hisBA, MS and PhD in com-puter science and engineeringin 1973, 1975 and 1978 respec-tively, all from Touhoku Uni-versity, Sendai, Japan. He wasa senior researcher in the De-partment of Computer Scienceat Touhoku University from 1978through 1985. He was an asso-ciate professor at Nagaoka Uni-versity of Technology from 1985to 1990. Since 1990 he has beena professor in the Department of

Artificial Intelligence at the Kyushu Institute of Technology.His interests include computer vision, image processing, pat-tern recognition and learning for multi-agents.

filtering and segmentation of digitized land use map images

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