TEMPLATE IMAGE PREPARATION TO REGISTER THE HIGH RESOLUTIONSATELLITE DATA
Kyaw Sann OO, Jong Hyeok JEONG and Masataka TAKAGIDepartment of Infrastructure Systems Engineering, Kochi University of Technology
Tosayamada, Kochi 782-8502. Japan
Tel: (81)-887-53-1040 Fax: (81)-887-57-2420
E-mail: 108007u@gs.kochi-tech.ac.jp
KEY WORDS: Registration, Ground Control Points, Template image, DSM
ABSTRACT: Template image preparation is a primary requirement in satellite image
registration. This technique could be automated by image matching method. Precision of image
registration is a tactical goal in remote sensing and geographic information sciences. It could beachieved by applying template image matching method. This experiment described on thepreparation of two types of template images. Band ratio normalization is belonged to first typeof template creation and simulated image based normalization is second method. Indeed, thestudy demonstrated that red band ratio could be applied to remove shade and shadow
disturbances of satellite images. On the other hand, artistic structures are very good groundobjects for ground control point selection because of those structures had consistency toseasonal land cover change.
1. INTRODUCTION
Selection of precise ground control points (GCP) are needed to register satellite images andgeographic information data. In reality, there are time and location constraints in collection ofGCPs by GPS or manually survey. To avoid those constraints, automatic GCPs collection isinterest way to remote sensing scientists. Satellite datasets could be helped to collect GCPs.
Even though, sun’s illumination affects are the main disturbances to land surface informationcollection in satellite remote sensing sciences. Firstly, shaded and shadow of images illuminatedby sun direction should be removed. Then, the shade/shadow free images could be as target andreference images.
The study describes process and technique of template image preparation, automatic groundcontrol points collection and image registration. In template image preparation, three datasets
are calculated based on satellite sensor’s multi-spectral bands. There are 13 ground control
points are collected from each of calculated reference templates using least squares matchingmethod. In this method matching calculation is iterated until initial input minimized to global
optimization. Red ratio normalization pair produced satisfies result when the accuracy isspecified to 5 pixels. There are several types of ground features are selected from vegetation,
bare land, artistic structures, and forest area. Particularly, artistic structures gave high matching
result.
2. STUDY AREA
The area is located downstream of Monobe river of Kochi prefecture in Japan. It covered 6 km
Χ 6 km of river bank regions including hilly, plain area buildup areas. There are three datasets
are used in this study two of them are acquired from satellite and other is digital surface model(DSM). Descriptions of satellite datasets are mention (table 1).

Table 1: Satellite images information.
ASTER VNIR (Visible and Near Infra Red)IKONOS
Wavelength (w/m2/sr/µm)
0.52 ~
0.6
0.63 ~
0.69
0.78 ~
0.86
08-Mar-2002(10:54)
Acquired Date/Time
Spatial Resolution
15 meters
Sun Azimuth Angle
149.319783°
Sun Elevation Angle
46.683752°
BAND
Band1
Band2
Band3N

Wavelength (w/m2/sr/µm)
0.506 ~
0.5950.632 ~
0.698
0.757 ~
0.853
15-Jun-2000(10:43)Acquired Date/Time
Spatial Resolution
1 meter
Sun Azimuth Angle
114.7576°
Sun Elevation Angle
69.25115°

BAND
Band2
Band3
Band4

3. METHODOLOGY
The first part of the methodology is image normalization. By applying image normalization,
shades and shadows of target ground objects could be simply removed. This removal method is
an importance pre-process to least squares matching.
Removal of shade and shadow affect from satellite datasets, selection of GCPs and how
distributed they are on overall area, and which transformation methods are main topics in thissatellite image registration study.
The study will be applied two methods to remove shade and shadow: band ratio normalizationand image simulation.
3.1 Normalization by Removal of Shade and Shadow
First, band ratio method (equation 1, equation 2) is used to remove shade and shadow affects.
Study is illustrated that band ratio could be eliminated shades and shadows from satellite
images. For comparison purpose, there two ratios are used to demonstrate good result. They are
infra-red ratio and red ratio of data (figure 1). Second method included a combination of redratio and DSM’s shade and shadow (equation 3).
InfraRed/(GREEN+RED+InfraRed)
Infrared ratio
For ASTER:
band 3/ (band 1+ band 2+ band 3N)
For AKONOS:
band 4/ (band 2+ band 3+ band 4)-----1

RED/(GREEN+RED+InfraRed)
Red ratio
For ASTER:
band 2/ (band 1+ band 2+ band 3N)
For AKONOS:
band 3/ (band 2+band 3+band 4)-----2By applying band ratio normalization shade and shadow from respective image were removed.
In the case of ASTER image, the study used three bands as two visible and one near infra red.
The three spectral wavelengths are ranging from 0.52 µm to 0.86 µm and IKONOS have 0.506µm to 853µm.

IKONOS Red normalized + DSM relief
ASTER Red band
Figure 1: Band ratio results, ASTER red band and DSM.
In the case of shaded relief, it was created from digital surface model (DSM) data by applying
sun vector of target satellite images. Afterwards, those shaded relief image and red ratio image
are mathematically added each other to output required target image.
3.2 Normalization by Simulation of Shade and Shadow
DSM (relief) +RED/(GREEN+RED+InfraRed)
Red ratio +relief-----3
For ASTER:
DSM (relief) + band 2/ (band 1+band 2+ band 3N)
For AKONOS:
DSM (relief) + band 3/ (band 2+band 3+ band 4)
A satellite image is simulated by composition of spectral, shade and shadow (figure 2). Spectralof land surface reflectance was collectedfrom reference satellite image by red
ratio. Shadow and shaded disturbance ofreference image were removed by band
ratio normalization. Subsequently,
shaded relief image was created fromdigital elevation model (DSM) data. In
this creation, shade and shadow wereextracted by giving sun vector ofASTER image to DSM data. The
extracted shade and shadow images
were added mathematically to getshaded relief image. Finally, shadedrelief and red ratio image were added.
The calculation produced a simulated target image. I could be match with ASTER’s original redband.
3.3 Selection of GCPs

GCPs are selected by least squares image matching method (equation 4). At first, three set ofimage templates (figure 3) are created from reference image based on initial conditions/values.
GroundBreakerLandmarkCross

Building

RoofRoad

Trees

ForestBridgePlotTriangle

Building2

Figure 3: Image templates for red ration plus DSM.
The program searched a matched pair of value from target images and output it as automatedselected GCPs.
gt(x,y)=h0+h1+gs [(a0+a1x+a2y),(b0+b1x+b2y)] --- 4
where:
gt: target window; gs: template window; h0, h1: unknown coefficients forbrightness adjustment; a0, a1, a2, b0, b1, b2: unknown coefficients for geometrictransformation
The equation is nonlinear equation; hence the equation was linearized by Taylor series. Initialvalues for unknowns were input to the linearized equation, and then iterative calculation carriedout until the correction of the initial input minimized to be fall in global optimization.

3.4 Registration
There are many transformation methods to do geometry correction such as Helmalt, affine,projective. Apart from other methods, the 3 dimension projective transformation (equation 5) isthe best fit to the study area because of the area had hilly area, plains and unevenly distributed
buildup area.
u= (a1x+ a2y+ a3z+ a4)/( b1x+ b2y+ b3z+ 1)
v= (a5x+ a6y+ a7z+ a8)/( b1x+ b2y+ b3z+ 1) ------------ 5
where:u, v = image coordinate; x, y, z = ground coordinate and a1, a2, a3,a4, a5,a6, a7,a8, b1, b2, b3 are unknown coefficients
4. RESULTS
The outputs of least squares matching are listed (table 2). There are 13 features are selecteddistributing over whole images.
Table 2: Initial values and LSQ output.
ID
123
45678910111213

IR ratio
Red ratio
Red ratio + relief
FeatureInitial U Initial V uv
uv
uv
Ground3845
753 3840.07
754.83 3839.51
757.70 3842.17
764.06
Breaker
4276
5505 4261.66 5505.98 4290.60 5498.64 4298.96 5501.91
Landmark
2930
5122 2930.84 5127.07 2926.97 5120.82 2929.88 5124.13
Cross4275579329.55 5536.22
435.14 5582.86
391.82 5564.46
Building
1067
2821 1067.28 2810.31 1068.79 2816.83 1067.61 2809.95
Roof2242
2241 2240.41 2242.89 2240.93 2243.73 2239.94 2243.59
Road5251
3451 5256.98 3456.86 5251.41 3450.67 5241.61 3443.53
Trees
4727
4831 4726.59 4821.57 4727.95 4821.50 4726.33 4816.53
Forest2896
3256 1765.39 5805.13 2891.25 3258.09 2996.63 3183.68
Bridge1762
5804 1764.12 5806.04 1767.57 5786.43 1764.12 5806.04
Plot4291
435 4297.91
438.00 4315.10
442.73 4283.42437.83
Triangle
5783
4816 5795.59 4832.39 5796.36 4821.36 5803.22 4815.96
Building2
1921
420 1912.59
430.39 1914.93429.71 2188.18
325.25

5. DISCUSSION
The study observed that it is very difficult to identify the small features such as bridges, roadcross sections, houses, trees on ASTER images. It affected to selection initial values. Based onmatching results (table 3), red ratio pair gave good result compared with others. It could beconcluded that vegetation spectral is highly influence over image matching. Apart from band
ratio discussion, the study could be gave some decision based on features. When roof is giving
the best control for all matching pairs; breaker, cross, trees, plot, triangle and building2 gavehigh deviation.
A constrain in the study is influence of land cover changes by acquisition date/time (table 1).
Study observed that seasonal land covered meanly affected to matching result of near infrared
ratio.
As future work, it could be possible to improve by adding more control points from moreground objects.

Table 3: Deviation of LSQ result from initial values.
IDFeatures

12
3
4
5
6
78
910
11
1213

GroundBreaker
Landmark
Cross
Building
RoofRoad
Trees
ForestBridge
PlotTriangle
Building2IR ratioΔuΔv
4.93
-1.83
14.34
-0.98
-0.84
-5.07
97.45
42.78
-0.28
10.69
1.59
-1.89
-5.98
-5.86
0.41
9.43
1130.61 -2549.13
-2.12
-2.04
-6.91
-3
-12.59-16.398.41-10.39Red ratio
ΔuΔv
5.49
-4.7
-14.6
6.36
3.03
1.18
-8.14
-3.86
-1.79
4.17
1.07
-2.73
-0.41
0.33
-0.95
9.5
4.75
-2.09
-5.57
17.57
-24.1
-7.73
-13.36
-5.36
6.07
-9.71

Red ratio+Relief
ΔuΔv
2.83
-11.06-22.96
3.09
0.12
-2.13
35.18
14.54
-0.61
11.05
2.06
-2.59
9.39
7.47
0.67
14.47
-100.63
72.32
-2.12
-2.04
7.58
-2.83
-20.220.04-267.18
94.75

Reference:
Gruen, A. (1984). Adaptive Least Squares Correlation: A Powerful Image Matching Technique.
A Afr J of Photogrammetry, Remote Sensing and Cartography 14(3), 175-187.
Jeong, J. H. (2007). Crack Monitoring Using a Digital Camera and in Image Total Station. TheInternational Symposium on Social Management Systems (ISMS2007), Infrastructure
Engineering.
濱田哲伸(2004). 標高データ を用いた不可視判定プログラム開発と その応用. 社会システム工学科 4年, 高木研究室.
Oo, K.S (2007). Automated registration with high to medium resolution satellite data. TheInternational Symposium on Social Management Systems (ISMS2007), InfrastructureEngineering.
Oo, K.S (2007). Digital Surface Model based Image Matching for Image Registration.
Geoinformation Forum Japan 2007, Applications of surveying Technologies.
Takae KADOTA, et al. (2005). , “Suitable Type of Ground Control Point for High-resolution
Satellite Imagery” oral paper of the 22th Asian Conference on Remote Sensing, 2005, RemoteSensing.