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					CMSC 190 SPECIAL PROBLEM, INSTITUTE OF COMPUTER SCIENCE                                                                                          1




Elevation Profile Visualization and Matching on the
                 Philippine Islands
                                                    Lawrence Namuco and Jaderick Pabico



   Abstract – This study proposes an approach for ren-                            from SRTM data. Lastly, a profile matcher would be created
dering topographic visualizations and for matching input                          to match the generated profiles from input images and from
images of land forms of the Philippine Islands to Shuttle                         the SRTM database. From this, one can know the location
Radar Topography Mission (SRTM) data. The approach                                of the land mass described in the image. The application’s
utilizes the use of triangle strips for fast rendering of                         main task is to model any specific area in the Philippines
visualizations of the different locations in the Philippines                      accurately based from the available SRTM data. When this
and the use of formulated features for matching of profiles                        is accomplished, the application could basically generate a
from input images of land masses and from the SRTM                                database of elevation maps of all known areas recorded in
database.                                                                         the SRTM data of the Philippines.
                                                                                     The creation of this application can provide a ground for a
                           I. I NTRODUCTION                                       lot topographic applications. The elevation profiles that could
                                                                                  be generated from the application can be used in classifying
   In the year 2000, NASA launched the Shuttle Radar To-                          unidentified land forms present on the surface of the country.
pography Mission (SRTM) which collected the first high high-                       The elevation profiles can serve as a basis for the feasibility
resolution digital elevation data of the whole world. Nowadays,                   of infrastructure construction on certain areas. The elevation
various versions of SRTM data have come out and are now                           profiles can also be used for routing purposes for hobbyists
widely available in the Internet. With the possession of this                     such as hikers, tri-athletes, and road trippers. The profile-
kind of data, one can study and analyse different topographic                     matching feature can be used on location detection, specifically
patterns present in a certain area. Also, the emergence of                        for search and rescue missions.
SRTM data has brought several geographical applications into
existences that were deemed impossible to create in the past.
                                                                                                         II. O BJECTIVES
Different functionalities and features were offered by these
applications that contributed to the improvement of distinct                        1) To create an application that can create a 3D panoramic
topographic databases and formulation of unique real-world                             visualization of any given location in the Philippines
simulations. One application of SRTM data that has not been                            based on SRTM data;
fully explored yet is it use on elevation profiling in the                           2) To correctly extract features from input images of high
Philippines.                                                                           elevation land masses using object segmentation and
   The Philippines, being an archipelago, has varying degrees                          edge detection;
of land elevation. As a result of this, many kinds of land                          3) To accurately match generated profile data from input
forms such as mountain ranges, hills, and valleys are present                          images to profile data from the SRTM database;
in the country. It is a tough task for anyone to keep track
of every existing land form in a specific area especially for                                III. R EVIEW OF R ELATED L ITERATURE
a country as big and as diverse in terms of land forms                               Several studies have been made regarding the generation of
such as the Philippines. Elevation profiling helps a lot on                        realistic graphical illustrations based on SRTM data. Basically,
this case. Elevation profiling is the process wherein available                    digital elevation models (DEM) such as SRTM data are basic
digital elevation information is extrapolated and transformed                     ingredients of any geo information system. SRTM data was
into distinct elevation models that can be further interpreted,                   produced from an 11-day space flight mission governed by
analyzed and utilized for other scientific and experimental                        the National Geospatial-Intelligence Agency (GIA) and the
purposes.                                                                         National Aeronautics and Space Administration (NASA). The
   One of the main goals of this study is to create an ap-                        lack of available worldwide DEMs and the great need for
plication in wherein users can view 3D panoramic elevation                        updated global topographic data that can be applied and used
models of the different parts of the Philippines given a range                    for conducting modern studies on the earth sciences were the
of longitude and latitude values. Another goal is to create                       main trigger for the mission (Farr et al., 2001) [8].
a profile-generating feature in that will allow the generation                        The creation of an application program which generates
of elevation profile data extracted from an input image of a                       realistic visualizations of land masses and bodies of water is
land form and elevation profile data that will be generated                        practically important because they can be used for allowing
                                                                                  a basis for different civilian and military field activities and
   Presented to the Faculty of the Institute of Computer Science, University
of the Philippines Los Ba˜ os in partial fulfillment of the requirements for the
                         n                                                        aviation systems. In a study by Hermocilla and Pabico (2010)
Degree of Bachelor of Science in Computer Science                                 [2], a software tool called FlowViz was designed to model,
CMSC 190 SPECIAL PROBLEM, INSTITUTE OF COMPUTER SCIENCE                                                                             2



simulate, and visualize surface water flow using SRTM data          were used in the study. Among the seven edge detectors,
specifically for the Philippines. The inspiration in creating       Canny performed best followed by Rothwell although the other
the software was largely based from country’s experience           edge detectors also had good results.
against recent typhoons which caused great physical damage           The discussion of these previous studies clearly implies that
to infrastructures and many casualties among humans. They          applications involving the use of SRTM data in modeling
aimed to create a tool for generating information that can be      topographic representations is relatively not new. However,
used for planning against floods and were successful in doing       only a few studies involving these applications are centered
it.                                                                specifically for the Philippines.
    The importance of topographic visualization applications is
further emphasized in the study by Valeriano et al. (2006)                           IV. M ATERIALS AND M ETHODS
[4] about modelling small watersheds in Brazil with SRTM
                                                                     The application was coded in C++. It is composed of 3
data. The original SRTM data was processed to make smoother
                                                                   major parts namely:
DEMs. The refined DEMs still contained the original drainage
features which were used for the drainage modeling. It is            1) Visualizer
reflected on this study that the use of DEMs such as SRTM             2) Profile Generator (Contour Extraction, Feature Extrac-
data is a good approach in characterization of landscape                tion)
attributes.                                                          3) Profile Matcher
    Identifying unique land masses is also one of the major
applications of SRTM data. Dragur and Blaschke (2008) [3],
in their study regarding terrain segmentation and classification,
devised a way to accurately sort SRTM 3 data into specific
land forms. Many topographic variables such as elevations,
slope gradients, slope aspects, and curvatures were extracted
from the SRTM data they used which served as the basis in
the process of identification. It is clearly reflected upon this
that SRTM data serves as a reliable tool in characterizing
unique land forms in the field of geomorphology. Besides            Fig. 1.   Esri File Format
its application on geomorphology, the use of SRTM data can
also be applied on volcanology, hydrology, forest ecology, and        The input data that was used for the program was SRTM
glaciology (Zandbergen, 2008) [5].                                 4.1 data from the CGIAR (Consultative Group on International
    There are many ways to construct graphical models given        Agricultural) website. SRTM 4.1 data is formatted with a raster
a set of data points. One way is via ray tracing. According to     GIS file format which was developed by Esri (see Fig. 1).
Cook and Carpenter (1984) [6] on their study on distributed        The first two columns indicate how many columns and rows
ray tracing, ray tracing is one the refined methods in graphics     the raster has while the third and fourth columns indicate
rendering. In a book by van der Ploeg (2007) [9], ray tracing      the starting latitude and longitude values of the raster. The
was defined as a graphics rendering technique for generating        5th column indicates the gap between each cell of the raster.
realistic images wherein a path of light is followed simulating    Every value in the raster represents a single cell in an elevation
natural lighting effects. Ray tracing is a global illumination     representation. One degree of latitude and longitude value was
rendering method, meaning light coming from all angles and         represented by 1200 columns and 1200 rows of height values.
surfaces are all taken into account during the image generation    The Philippines’ whole SRTM data is comprised of a total
process. This results into the addition of more advanced           of eight files each consisting of data matrix of 6001 x 6001
effects like reflections, refractions, and shadows in a generated   height values.
picture.
    In a study by Aluning and Hermocilla (2010) [7], three
algorithms were implemented in generating and visualizing          A. Visualizer
terrains. The algorithms that they used were the Midpoint             The visualizer’s task is to a generate 3D panoramic visual-
Displacement algorithm, Fault Formation algorithm, and the         ization of any given location in the Philippines based on SRTM
Perlin Noise algorithm. Based on the conclusion of their study,    data. The first part of the visualizer was designed to get three
each algorithm had its strengths and weaknesses.                   input values from the user, a longitude value, a latitude value,
    The application of image processing methods on topo-           and a scope value. The longitude and the latitude values are
graphic maps is also not a new concept. To extract features        the coordinates of the location the user want to project while
from topographic maps, different image processing methods          the scope value is the range of the projection starting from the
such as edge detection and object segmentation is required. In     coordinates specified by the user. Once an input of a latitude, a
a study by D.P. Argialas and O.D. Mavrantza (2004) [1] on the      longitude, and a scope value is given, the height values within
comparison of edge detection techniques for the extraction of      the specified scope starting from the coordinates will be saved
geologic features on DEMs, several edge detection techniques       in a text file which then will be converted to a TrueVision (tga)
were discussed. Different edge detectors such as Canny, Roth-      24 bit uncompressed RGB image, a common image format for
well, Black, Bezdek, Iverson-Zucker, EDISON, and SUSAN             height maps (see Fig. 3). The image will then be fed into the
CMSC 190 SPECIAL PROBLEM, INSTITUTE OF COMPUTER SCIENCE                                                                              3



                                                                    from vertices to draw the visualization. A triangle strip is a
                                                                    series of connected triangles sharing vertices which allows fast
                                                                    rendering (see Fig. 4). On the other hand, a display list is a
                                                                    group of OpenGL commands that have been stored for later
                                                                    use. All vertex and pixel data are evaluated and copied into
                                                                    the display list memory once a display list is created. One
                                                                    advantage of display list is that it is one of the fastest methods
                                                                    to draw static data because it can be use repeatedly to draw
                                                                    frames.
                                                                       These are the simplified steps for rendering the visualiza-
                                                                    tion:
                                                                       1) Set initial point of the visualization
Fig. 2.   Visualizer Flow                                              2) Compute for normals
                                                                       3) Create the display list
                                                                       4) Create n-1 strips; n = length of visualization
                                                                       5) Call the display list

                                                                    B. Profile Generator




Fig. 3.   A height map of Mt. Mayon



visualizer via a tga library which contains functions for loading
the image properly.
   The tga image that will be produced from the initial process
                                                                    Fig. 5.   Generator Flow
will be loaded. The height of each pixel from the image will be
measured based on each pixel’s intensity for height scaling and
                                                                       The profile generator’s task is to generate profiles from the
later on for the computation of normals. The computation of
                                                                    SRTM database and from the input images that will be fed into
normals will be needed to simulate light for the visualization.
                                                                    the profile matcher. For the profile data for both the SRTM
A normal is a vector which contains information about the
                                                                    database and from the input images, two features will be used:
lighting of certain surface. Normals in OpenGL is defined per
face of per vertex. The definition used in this study is per            1) Ratio of heights starting from the outermost left to the
vertex normal computation because it offers more smoothness.               center and heights starting from the outermost right to
                                                                           the center
                                                                                        lef t = heightsf romthelef t
                                                                                       right = heightsf romtheright
                                                                                            RATIO = left / right
                                                                        2) Summation of all heights divided by the width of the
                                                                                                    image
                                                                       The features used for the computation of profiles of the
                                                                    SRTM database is identical with the features used for the
                                                                    images. These are:
                                                                       1) Ratio of heights starting from the outermost left and the
                                                                           outermost right of the point of reference
Fig. 4.   Triangle Strips                                              2) Summation of all heights divided by the length of the
                                                                           segment considered in the computation
   Normals at vertices will be computed as the normalised sum          The first feature for both the input images and the SRTM
of all the normals for each face of the vertex. The face-per-       database was chosen to identify the weight of the heights
vertex were set to four for this study. Display lists will be       from both sides starting from the center for the contour and
created based from the triangle strips that would be generated      starting from the points of origin for the SRTM Database. On
CMSC 190 SPECIAL PROBLEM, INSTITUTE OF COMPUTER SCIENCE                                                                            4



the other hand, the second feature for both the input images
and the SRTM database was chosen to determine the volume
of the land mass described by the contour. Every longitude
and latitude point for every 6th row of the files in the SRTM
database was extracted a profile. For each file in the SRTM
database, there were a total of 500500 profiles generated. From
these profiles, profiles of input images will be compared for
matching.
   Open Source Computer Vision (OpenCV), a library of
programming functions for image processing, was used for
processing the input images. The application was designed to
accept input images of land mass. The foreground (land mass)
must be separated from the background to be able to extract        Fig. 6.   Matcher Flow
a contour from it. To separate the background from the fore-
ground, object segmentation was needed to be implemented.
                                                                                                   TABLE I
The cvWatershed function of OpenCV was used for this. The
                                                                                        FPS R ATES OF V ISUALIZATIONS
cvWatershed function from OpenCV was based on one of the
variants of watershed segmentation, a non-parametric marker-                    Visualization     Min FPS   Max FPS     Avg FPS
based segmentation algorithm. Basically, the user would need                 Mt. Makiling (480)     13         17       14.8
                                                                              Mt. Mayon (480)       12         17       14.7
to mark the input image of which is the foreground and the                   Sierra Madre (480)     13         18       15.7
background. After marking, The foreground will be extracted                  Mt. Makiling (240)     51         64       61.4
while the background will be removed.                                           Mayon (240)         48         56       53.2
   These are the simplified steps for the Watershed Segmenta-                 Sierra Madre (240)     51         63       59.4
                                                                             Mt. Makiling (120)     169       196       186.6
tion algorithm:                                                               Mt. Mayon (120)       179       244       199.6
   1) Find the markers and the segmentation criterion for                    Sierra Madre (120)     125       160       149.7
      region splitting which is most often the contrast or
      gradient
   2) Perform watershed with the two elements (flooding of                            V. R ESULTS AND D ISCUSSION
      the surface from its minima and preventing the merging       A. Visualizer
      of waters coming from different sources)                        For the demonstration of the visualizer, three visualizations
   After foreground separation, the contour of the foreground      were chosen to be rendered within three different scopes:
must be extracted. The cvCanny function of the OpenCV was          Sierra Madre (16.22, 121.85) (see Fig. 7), Mayon Volcano
used to extract the contour of the foreground. The cvCanny         (13.25, 123.68) (see Fig. 8), and Mt. Makiling (14.16, 121,24)
function from OpenCV implements the famous Canny Edge              (see Fig. 9). Each location was rendered within scopes of
Detection (CED) algorithm, one of the best edge detection          120, 240, and 480. Their frame rates were recorded using
algorithms. The CED algorithm is known to be the most              Fraps, a free-ware benchmarking program to determine the
optimal edge detection algorithm among all existing.               performance of the visualizer. The minimum and maximum
   These are the simplified steps for the Canny Edge Detection      FPS rate and the averages of FPS rates per scope of each
algorithm:                                                         visualization were also recorded.
   1) Reduce noise in the image by the help of a Gaussian             Table 1 compares the frames-per-second rates (FPS) of
      mask before locating any edges                               different visualizations in different scopes. The application was
   2) Compute the edge strength by taking the gradient of the      run on a Core i5 560M 2.56 GHz, 2GB RAM computer with
      image                                                        Nvidia 310M graphics. Out of the three visualizations, Sierra
   3) Find the edge direction and relate it to a direction that    Madre obtained the lowest FPS rate. On the other hand, Mayon
      can be traced in the given image                             got the highest FPS rate. Los Banos’s FPS rate sat between the
   4) Trace the edges                                              two others. Table 1 shows that FPS rates tend to dip as scopes
   After the contour of the foreground has been traced, the        get wider. Visualizations with a a lot high elevation points such
features will be extracted from it based from the heights          as Sierra Madre tend to run slower than visualizations with
(northernmost points) of the contour.                              mostly flat surfaces like the visualization of Los Banos. All
                                                                   of the visualizations were rendered correctly and accurately.
C. Profile Matcher                                                  Correct lighting was observed and no unusual geo-shapes were
                                                                   observed.
   The profile matcher’s task is to compare a profile generated
from an input image to profiles in the database and find a
match. A comparison threshold was set to apply filtering of         B. Profile Generator
coordinates. If a match is found, the corresponding latitude         For the profile generator, eight images of different high land
and longitude values will be appended to the list of coordinates   masses were collected for the generating contours (see Fig.
which passed through the comparison threshold.                     10). Images were cropped accordingly so that the lowest points
CMSC 190 SPECIAL PROBLEM, INSTITUTE OF COMPUTER SCIENCE                                                                                      5




Fig. 7.   Sierra Madre Visualization
                                                                          Fig. 9.   Mt. Makiling Visualization




Fig. 8.   Mt. Mayon Visualization



in the images were the land masses’ bases. All of the images’
contours were extracted correctly. The profile data of each                Fig. 10. From L-R, top to bottom: Mayon, Isarog, Canlaon, Apo, Taal,
contour were recorded in Table II.                                        Kitanglad, Matutum, Makiling


C. Profile Matcher                                                         coordinates out of the eight images. The other two images
   For the profile matcher, the contours produced by the profile            obtained at least a half degree difference against their original
generator were used. Each profile was compared to all of the               latitude coordinates but were close enough to qualify for their
computed profiles in the SRTM database.                                    original longitude coordinates. Correct contours were extracted
   Table III presents the closest coordinates retrieved by the            from all eight images.
application basing from the profile from the SRTM database
and the original coordinates of the land forms in the input                           VI. C ONCLUSION AND F UTURE W ORK
images. The distances between the result coordinates and the                 The Shuttle Radar Topography Mission (SRTM) data has
original coordinates were also recorded. As shown in it, all              a lot of practical uses. Two of those uses were achieved in
profiles of the contours resulted to near matches for at least             this study. Firstly, a visualizer for the Philippine Islands was
the latitude coordinate or the longitude coordinate. Six images           developed to provide a 3D model of any given location in the
were accurately matched by the profile matcher to their correct            Philippines. The use of triangle strips proved to be a fast and
                                                                          efficient technique for generating topographic visualizations.
                                  TABLE II
                                                                          Secondly, a profile generator and matcher were created to
                               P ROFILE DATA
                                                                          generate profiles from input images and from the SRTM
                                                                          database for profile matching. The Watershed Segmentation
                         Input         Ratio   Volume                     Algorithm and The Canny Edge Detection algorithm proved
                        Mayon          1.03    23.29                      to be a good combination of algorithms for extracting the
                        Isarog         0. 37   13.07
                       Canlaon         0.98    37.57                      contours from the input images. Also, the features formulated
                          Apo          1.04    24.28                      for the generation of the profiles proved to be good features
                          Taal          .50    19.81                      based on the results of the profile matching tests done in this
                       Kitanglad       0.98    52.77
                       Matutum         0.90    25.4
                                                                          study which produced nearly accurate results.
                       Makiling        0.85    30.07                         Following the observations described in this study, a number
                                                                          of enhancements can be done to improve the application
                               TABLE III                                  developed in this study. For the first part, the addition of
                       M ATCHING OF C OORDINATES                          features like texture, foliage, and additional lighting could take
                                                                          this application to the next level. Also, with the availability of
        Input            Result              Original      Distance (m)
       Mayon        (13.27, 123.69)      (13.25, 123.68)       24.60      different rendering techniques, a different one could be used
       Isarog       (13.79, 123.61)      (13.67, 123.37)      295.16      to enhance the quality of the visualizations. For the second
      Canlaon       (10.46, 123.15)       (10.4, 123.12)       73.79      part, adding more good features to work on would increase
         Apo         (6.91, 125.25)       (6.98, 125.27)      264.46
         Taal       (13.41, 120.82)      (13.87, 120.92)      517.81
                                                                          the success rate of matching and will also increase the filtering
      Kitanglad      (8.11, 124.80)       (8.10, 124.92)      132.46      rate. Lastly, given the availability of one country’s SRTM data,
      Matutum        (6.35, 125.08)       (6.58, 125.08)        253       the approaches used in this study can be applied to other
      Makiling      (14.83, 121.35)      (14.14, 121.19)      779.14      countries as well.
CMSC 190 SPECIAL PROBLEM, INSTITUTE OF COMPUTER SCIENCE                         6



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[2] Pabico J. and Hermocilla J. Identifying catchment areas near selected
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[4] Valeriano M., Kuplich M., Storino M., Amaral B., Mendes J., and
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[5] Zandbergen P. Applications of shuttle radar topography mission elevation
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[6] Cook R., Porter T., and Carpenter L. Distributed ray tracing. Computer
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[7] Aluning R.C. and Hermocilla J. Terra: A 3d terrain generator and
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