Contour Line Tracing Algorithm for Digital Topographic Maps

Ratika Pradhan, Shikhar Kumar, Ruchika Agarwal, Mohan P. Pradhan & M. K. Ghose
International Journal of Image Processing (IJIP), Volume (4): Issue (2) 156
Contour Line Tracing Algorithm for Digital Topographic Maps
Ratika Pradhan ratika_pradhan@yahoo.co.in
Department of CSE, SMIT, Rangpo, Sikkim, INDIA
Shikhar Kumar shikarkum@gmail.com
Ruchika Agarwal ag.ruch@gmail.com
Mohan P. Pradhan mohanp_pradhan25@yahoo.com.sg
M. K. Ghose mkghose@smu.edu.in
Abstract
Topographic maps contain information related to roads, contours, landmarks,
land covers and rivers etc. For any Remote sensing and GIS based project,
creating a database using digitization techniques is a tedious and time
consuming process especially for contour tracing. Contour line is very important
information that these maps provide. They are mainly used for determining slope
of the landforms or rivers. These contour lines are also used for generating
Digital Elevation Model (DEM) for 3D surface generation from any satellite
imagery or aerial photographs. This paper suggests an algorithm that can be
used for tracing contour lines automatically from contour maps extracted from the
topographical sheets and creating a database. In our approach, we have
proposed a modified Moore’s Neighbor contour tracing algorithm to trace all
contours in the given topographic maps. The proposed approach is tested on
several topographic maps and provides satisfactory results and takes less time to
trace the contour lines compared with other existing algorithms.
Keywords: Topographic map, Contour line, Tracing, Moore neighborhood, Digital Elevation Map (DEM)
1. INTRODUCTION
Topographic map is a type of map that provides detailed and graphical representation of natural
features on the ground. Topographic maps conventionally show topography, or land contours, by
means of contour lines. These maps usually show not only the contours, but also any significant
streams, other water bodies, forest covers, built-up areas or individual buildings (depending on
scale) and other features. These maps are taken as reference or base map for many Remote
Sensing and GIS based application for generating thematic maps like drainage maps, slope
maps, road maps, land cover maps etc. The important and distinct characteristic of these maps is
that the earth’s surface can be mapped using contour lines. Digitization or vectorization process
for generating contour map for a state like Sikkim where there is large variation of slope takes
tremendous amount of time and manpower. Many research works are currently being conducted

in this field to automate the entire digitization process. Till today, a fully automated digitization
process does not provide satisfactory result.
Contour lines are imaginary lines that join points of equal elevation on the earth’s surface with
reference to mean sea level or curves that connect contiguous points of the same altitude
(isohypse). These lines are depicted brown in color in topographic maps, and are smooth and
continuous curves with a width of three to four pixels. These lines runs almost parallel or they
may be taken as nonintersecting lines except in steep cliffs. However, along with contour line, the
topographic maps also contain text information overlaid on these lines. This makes the entire
automation of extracting and tracing contour lines from the contour maps more complex and
difficult.
Traditional method for vectorization of contour line involves mainly the following steps:
 Scanning paper topographic maps using high resolution scanner.
 Registration of one or more maps with reference to the nearest datum.
 Mosaicing or stitching various topographic maps.
 Vectorization of various contour lines manually using line tracing by rubber band method.
 Feeding depth information for each contour line.
 Generating digital elevation models (DEM) for 3D surface reconstruction.
Uses of computer and digital topographic maps have made the task simpler. Currently research is
being carried out on automatic extraction of contour lines from topographic maps that involves
following five main tasks.
 Registration of topographic map.
 Filtering for enhancing map.
 Color segmentation for extracting contour lines.
 Thinning and pruning the binary images.
 Raster to vector conversion.
The proposed work suggests a method that efficiently extracts contour lines, performs tracing of
contour lines and prepares a database wherein user can feed the height value interactively. In
this paper, we have proposed a modified Moore’s Neighbor contour tracing algorithm to trace all
contours in the given topographic maps. The content of the paper is organized as follows. In
section II we have summarized the related work carried out in this area. In section III, we have
discussed contour extraction and thinning algorithm. In section IV, we have discussed the original
Moore’s Neighbor contour tracing algorithm, followed by Modified Moore’s Neighbor Algorithm in
section V. Result and discussion in section VI provides detail result for study area and
comparison of these two algorithms. Finally Conclusion and future scope is given in section VII.
2. RELATED WORK
Many researchers have indulged themselves to come up with a technique to completely automate
information extraction from topographic maps. Leberl and Olson [1] have suggested a method
that involves the entire four tasks mentioned above for automatic vectorization of clean contour
and drainage. Greenle [2] have made an attempt to extract elevation contour lines from
topographic maps. Soille and Arrighi [3] have suggested image based approach using
mathematical morphology operator to reconstruct contour lines. Most of these procedures fail at
discontinuities. Frischknecht [4] have used hierarchical template matching algorithm for extracting
text but fails to extract contour lines. Spinello [5] have used geometric properties to recognize the
contour line that is based on global topology. It uses Delaunay triangulation to thin and vectorize
contour line. Zhou and Zhen [6] have proposed deformable model and field flow orientation
method for extracting contour lines. Dongjun et.al [7] has suggested a method based on
Generalized Gradient Vector Flow (GGVF) snake model to extract contour lines. In this paper we
have extended the work of Dongjun et.al [7] to trace the contour lines more efficiently and
automatically using Modified Moore’s Neighbor tracing algorithm. It also prepares databases of
these contour lines to feed the elevation value interactively. Since the topology of contour lines

are well defined i.e. a set of non-intersecting closed lines, it makes the tracing of contour lines
simpler.
There exists many contour tracing algorithms - Square tracing, Moore neighbor, Radial sweep,
Theo Pavlidis’ tracing algorithms[8] etc. but each algorithm has its own pros and cons. Most of
these algorithms fail to trace the contour of a large class of patterns due to their special kind of
connectivity i.e. contour family of 8 connected patterns (that are not 4 connected). Disadvantage
of these algorithms are that they do not trace holes present in the pattern. Hole searching
algorithms are first used to extract holes and then tracing algorithms are applied to each hole in
order to trace the complete contour. Another problem with this algorithm is defining the stopping
criterion for terminating an algorithm.
3. CONTOUR EXTRACTION AND THINNING
Contours are depicted as brown colored line in topographic maps usually of width four to five
pixel length. After removing noise in the input images, we have used color segmentation
technique to extract all the information given in brown color. There are many color spaces widely
used to view digital images but most commonly RGB color space is used for the satellite imagery
as it possesses compatibility with the computer displays. Since this color space is not
perceptually uniform, selecting range of values for brown color in all the three bands is difficult
and does not give satisfactory end result, therefore we have first transformed the satellite imagery
from RGB to HSV color space and then color segmentation was performed on HSV color space.
The color segmentation algorithm is given below:
ALGORITHM Color Segmentation on HSV color space
Input: A square tessellation T containing a connected component P of pixels in HSV color space.
Output: A sequence B(b1, b2, …, bk) of brown colored pixels.
Begin
 Set B to be empty.
 From bottom to top and left to right scan the cells of T until a pixel, s, of P is found.
 Set the current pixel point, c, to s i.e. c = s.
 While c is not in B do
 If hue_range of c between 0 to 0.11 and saturation_range of c between 0.2 to 0.7
o Insert c in B.
 End if
 Advance c to the next pixel in P.
 End while
End
The segmented information includes contours and altitude information. The filtered or segmented
image is then thinned using morphological thinning algorithm [9] given below.
 Divide the image into two distinct subfields in a checkerboard pattern.
 In the first sub-iteration, delete pixel p from the first subfield if and only if the conditions
G1, G2, and G3 are all satisfied.
 In the second sub-iteration, delete pixel p from the second subfield if and only if the
conditions G1, G2, and G3' are all satisfied.
Condition G1:

(1)
where
(2)
(3)
x1, x2, ..., x8 are the values of the eight neighbors of p, starting with the east neighbor and
numbered in counter-clockwise order.
Condition G2:
(4)
where
(5)
(6)
Condition G3:
(7)
Condition G3':
(8)
The processed image thus obtained contains broken contour lines, we have used broken contour
lines reconnection algorithm [7] based on GGVF to connect the gaps in contour lines.
4. MOORE NEIGHBOR CONTOUR TRACING ALGORITHM
Moore Neighborhood of a pixel, P, is the set of 8 pixels which share a vertex or an edge with that
pixel. The basic idea is: - When the current pixel p is black, the Moore neighborhood of p is
examined in clockwise direction starting with the pixel from which p was entered and advancing
pixel by pixel until a new black pixel in P is encountered. The algorithm terminates when the start
pixel is visited for second time. The black pixel walked over will be the contour of the pattern.

FIGURE 1: Working of Moore’s Neighbor tracing algorithm.
The main weakness of Moore Neighbor tracing lies in the choice of stopping criteria i.e. visiting
the start pixel for second time. If the algorithm depends on this criterion all the time it fails to trace
contour of large family of patterns. Mostly it uses Jacob’s stopping criterion i.e.
i. Stop after visiting the start pixel n times, where n is at least 2, or
ii. Stop after visiting the start pixel second time.
Figure 1 demonstrates the working of Moore Neighbor contour tracing algorithm for an input
pattern. In figure, line number indicates the iteration number of traversal. For the input pattern,
start pixel is encountered three times when the algorithm ends.
5. MODIFIED MOORE NEIGHBOR CONTOUR TRACING ALGORITHM
The original Moore Neighbor tracing algorithm is defined for contours of multiple pixel width. It
requires either visiting start pixel 2 times or use Jacob’s stopping criteria to terminate the
algorithm. In our algorithm the basic idea is: - When the current pixel is black, the Moore
neighborhood of P is examined in clockwise direction till no more black pixels are encountered.
Then, we move to the start pixel and the Moore Neighborhood of P is examined in an anti-
clockwise direction until no new black pixels are left. The algorithm for the Modified Moore’s
Neighbor tracing is given below:
ALGORITHM Modified Moore’s neighbor algorithm
Input: A square tessellation T containing a connected component P of black cells.
Output: A sequence B(b1, b2, …, bk) of boundary pixels i.e. the contour line. We define M(p) to
be the Moore neighborhood of pixel p, c denotes the current pixel under consideration i.e. c is in
M(p).
Begin
 From bottom to top and left to right scan the cells of T until a black pixel, s, of P is found.
 Insert s in B.
 Set the current boundary point, p, to s i.e. p = s.
 Set c to be the next clockwise pixel in M(p).

 If c is black
o Insert c in B.
o Set p=c.
 End if
 Advance c to the next clockwise pixel in M(p).
 End while
 Insert s in B.
 Set p=s.
 Set c to the next anticlockwise pixel in M(p).
 If c is black
o Insert c in B.
o Set p=c.
 End if
 Advance c to the next anticlockwise pixel in M(p).
 End while
End
FIGURE 2: Working of Modified Moore’s Neighbor tracing algorithm.
Figure 2 demonstrate the working of Modified Moore’s Neighbor tracing algorithm. Line number in
the figure indicates the pixels from where they are traced from. The algorithm terminates when no
more black pixel in an input pattern is left. Unlike original Moore’s Neighbor tracing algorithm back
tracking is not used here and is not dependent on the stopping criterion used by original Moore
algorithm or Jacob stopping criterion. The start pixel is encountered only twice for terminating the
algorithm for every pattern.
6. RESULTS AND DISCUSSION
The study area taken into consideration is in and around Majitar, East Sikkim, situated between
27o
09’00” and 28o
13’48” north latitudes and 88o
29’24” and 88o
36’00” east longitude. The

topographic map for the study area is on scale of 1:250000. Figure 3(a) is the topographic map of
the study area. Figure 3(b) is the result of applying color segmentation algorithm. Figure 3(c) is
the result of applying broken contour lines reconnection algorithm based on GGVF followed by
thinning. 3(d) is the result of Moore Neighbor tracing using Jacob stopping criterion, 3(e) is the
result of Modified Moore Neighbor tracing algorithm. Table 1 is the database prepared for the
contour map traced using proposed method.
The efficiency of any algorithm entirely depends on the choice of stopping criterion. Original
Moore Neighbor tracing algorithm using Jacob stopping criterion that needs N + (n-1) * (N-1)
pixels to be traversed, where n is the number of times that the start pixel is visited and N is the
number of black pixels that forms a contour line. The choice of scanning anticlockwise after we
move to the start pixel in our algorithm is to avoid detection of black pixels already encountered in
the clockwise scanning. Since we do not use backtracking, for every detection of black pixel,
there is a maximum overhead of checking 6 pixel locations (worst case) before finding a black
pixel. Using the Moore-neighbor algorithm, since the algorithm has to retrace the start pixel, there
is an overhead of redetection of each and every already traced pixel.
In Modified Moore Neighbor algorithm we have removed the dependency of reaching the start
pixel in order to stop the algorithm i.e. start pixel is no longer required as a landmark to indicate
the end of algorithm. The proposed algorithm does not require hole searching algorithm to detect
holes in the input pattern. The drawback of this algorithm however is consistent checking of
every pixel encountered in the Moore Neighbor to decide whether it has been encountered before
or not. For very large size images, checking pixels every time could be time consuming and
costly. Another disadvantage of the algorithm is that it works only on contour lines of single pixel
width. Hence the extracted contour map has to undergo thinning.
Figure3 a) Topographic map of the study area b) Contour Extraction using Color Segmentation c) Contour
reconstructed using broken contour lines reconnection algorithm [7] based on GGVF d) Result obtained
using Original Moore’s Neighbor tracing algorithms where holes are not detected e) Results obtained using
Modified Moore’s Neighbor tracing algorithms with detected holes.

No. of contours: 42
Starting Point End Point
Serial No: x y X Y Elevation
1 15 635 16 471 4000
2 15 598 16 494 3600
3 15 562 16 515 3200
4 15 446 52 644 2800
5 15 433 108 646 2400
. . . . . .
. . . . . .
. . . . . .
TABLE 1: Database generated for the result obtained.
7. CONCLUSION AND FUTURE WORK
The Modified Moore Neighbor algorithm works on pre-thinned contour lines (single pixel width).
Its efficiency over the original Moore Neighbor algorithm lies in the stopping criterion as the
complexity is greatly reduced and hole searching algorithm is not required which further reduces
the time complexity. In order to overcome the disadvantage of rechecking black pixels in
proposed algorithm, we can check whether the contour line on which the pixel exists has been
traced or not rather than checking the pixel. This work can be refined further by automatically
extracting altitude value from the topographic sheet by using and automated OCR method.
8. ACKNOWLEDGMENT
We would like to thank All India Council for Technical Education (AICTE) for funding the project
title “Contour Mapping and 3D Surface Modeling of State Sikkim” fully sponsored by All India
Council of Technical Education, Govt. of India vide order no- 8023/BOR/RID/RPS-44/2008-09.
We also like to thank Dr. A. Jeyaram, Head, Regional Remote Sensing Service Centre (RRSSC),
IIT campus, Kharagpur for his valuable comments and support.
9. REFERENCES
[1] F. Leberl, D. Olson, “Raster scanning for operatioal digitizing of graphical data”,
Photogrammetric Engineering and Remote Sensing, 48(4), pp. 615-627,1982.
[2] D. Greenle, “Raster and Vector Processing for Scanned line work”, Photogrammetric and
Remote Sensing, 53(10), pp. 1383-1387, 1987.
[3] P. Soille, P Arrighi, “From Scanned Topographic Maps to Digital Elevation Models”, Proc. of
Geovision, International Symposium on Imaging Appications in Geology, pp.1-4,1999.
[4] S. Frischknecht, E. Kanani, “Automatic Interpretation of Scanned Topographic Maps: A
Raster – Based Approach”, Proc.Second International Workshop, GREC, pp.207-220, 1997.
[5] S. Salvatore, P. Guitton, “Contour Lines Recognition from Scanned Topographic Maps”,
Journal of WSCG, pp. 1-3, 2004.
[6] X. Z. Zhou, H. L. Zhen, “Automatic vectorization of comtour lines based on Deformable model
and Field Flow Orirntation”, Chiense Journal of Computers,vol 8, pp. 1056-1063, 2004.
[7] Dongjum Xin, X. Z. Zhou, H.L.Zhen, “Contour Line Extraction from Paper- based Topographic
Maps”.
[8] G. Toussaint, Course Notes: Grids, connectivity and contour Tracing
<http://jeff.cs.mcgill.ca/~godfried/teaching/pr-notes/contour.ps>.
[9] Lam, L., Seong-Whan Lee, and Ching Y. Suen, "Thinning Methodologies-A Comprehensive
Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 14, No. 9,
September 1992, page 879.

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