Digital Surface Model based Image Matching for Image Registration○Kyaw Sann OO, Jong Hyeok JEONG and Masataka TAKAGI
Kochi University of Technology, JAPAN
Abstract: Image registration technique is the basic need of digital image processing. Image matching process
should be done before image registration. This study could be conducted image registration by matching twoimages, the reference and satellite images. The reference image could be simulated from Digital Surface Model.
The satellite image will be any type of image with same location. Firstly, the shadow image and shaded relief
image were calculated from elevation data using acquisition parameters of satellite image; secondly, the reference
image was simulated by adding shade and shadow images. The simulated image was match with input satellite
images; after all, the registration was carryout. The result gives 2 pixels RMS error.
Keyword: Ground Control Points, RMS, Digital Surface Model, Least Square Matching

1 Introduction
Image registration task is a basic requirement of digital
image processing in remote sensing andphotogrammetry. Registered images could be appliedin many applications; support that study on landuse/land-cover change in forestry and agriculture;investigation of land movement in infrastructureengineering, civil work, and oil/gas exploration. All
those image analyzing applications should be respected
to prior registration. Traditionally, images are register
using manual selected ground control points (GCPs).
GCP points could be lat/long values of respected earth
location or x, y position in another map of the earth.
The selection of GCP points is tricky work when theinterested area has not included the remarkablefeatures. Some of interest areas could be dense forest,hilly area without river indicator, or planed-flat land. Itis very difficult to find GCP points on thathomogenous area; in regarding to this, there is a way
to register images by least square method, A. Gruen(1984). It is a powerful and an adaptive technique toregister images by matching the template (searchingpatch) with input images. The research work is applied
this matching method using simulated satellite image.
Initially, a satellite image was simulated from thedigital surface model (DSM) with high spatialresolution; afterwards, the searching patches aredefined on the simulated image. Subsequently, thematching method was applied to the input image withrelated template.

1.1 Problems of interest
The problems of interest in this research are:• Accurately registration to the satellite images
using digital surface model data•

Efficient used of DSM data to solve theproblem affect different satellite images by sun
direction

1.2 Objectives
The objectives of this study are:
• To simulate satellite image from DSM• To match images for image registration
purpose• To investigate the use of simulated DSM
image in image processing as reference

2 Methodology of Image Matching
Initially, shadow was extracted from the elevation data,
applying with input image’s acquisition parameters.
Firstly, sun elevation angle and sun azimuth angle
were calculated from the acquisition time and study
area coordinate; afterwards, the values are input tocalculate shadow and shade relief. A simulated image
was produced by adding the shade and shadow data.
The simulated image should be resample as the size ofinput image. Secondly, templates (15×15 pixels) wereselected from the simulated image. The selected
patches could be matched with input image by giving
initial value to each patch. The least squares matching
method was used in the matching process. Overall
methodology was shown in the figure (1).

spectral; bands red, green and blue. The RGB
color was transformed to hue, saturation, lighting(intensity). The intensity band was extracted tomatch with reference patches.

Figure 1: overall flow
At last, the result will be evaluated by comparing
visually selected values.

2.1 Data used
There are two type of data is using in this work. Thoseare elevation data as reference and ASTER image as
raw image.

2.1.1

Digital Surface Model (DSM)

DSM (RaMSE: Kokusai Kogyo CO., LTD.) is theacronym of digital surface model. It has 1 meterspatial resolution; including the actual surface highof buildings, trees, bushes and houses from the sea
level line. The left upper corner of data low landwith dark appearance, and the other side wascovered by hilly area. There are some buildings
and agriculture land in the middle part of the data.

Figure 3: part of ASTER image

2.2 Image simulation
A satellite image was simulated from DSM bycombining shadow and shade relief of elevation data.
Initially, Shadow image and shaded relief image
should be calculated.

2.2.1

Shadow

The movement of the shadow of the sun was able
to be calculated as show in figure (4). Firstly, solar
direction (sun elevation angle φ, sun azimuth angleθ) was calculated from local coordinate of study
area and acquired time of input image. The groundcoordinate (L, M, N) are calculate from solar
direction.

Figure 4: solar direction
Figure 2: Digital Surface Model (DSM)

2.2.2
2.1.2

Shade relief

ASTER image

The input data is a subset of a satellite image
acquired by ASTER sensor. It has medium spatialresolution (15 meters). The image has three

The calculated sun elevation angle and sun
azimuth angle are entered into the shaded relief
image creation process using image processing
software.

flatland

agriculture areabuild-up area

elevated areariver bankrivertrees

agriculture 2

Figure 5: simulated image

2.3 Matching
The simulated image and input image were matched byleast squares matching.
gt(x,y)=h0+h1+gs [(a0+a1x+a2y),(b0+b1x+b2y)]where; gt: target window
gs: template windowh0, h1: unknown coefficients for brightness
adjustment
a0, a1, a2, b0, b1, b2: unknown coefficients forgeometric transformation
The equation is nonlinear equation; hence the equation
was linearized by Taylor series. Initial values forunknowns were input to the linearized equation, andthen iterative calculation carried out until thecorrection of the initial input minimized to be fall inglobal optimization. The selected template patches andits locations are shown in the figure (5) and figure (6).
flatland

agriculture 1

build-up area

elevated areariver bankriver

agriculture 3

Figure 6: template location on ASTER image

3 Result of Image Matchingtrees

agriculture 2

agriculture 3

Figure 5: templates patches

Evaluation of this work was done by comparing
between two results of visual or least squares
matching approach.

3.1 Initial Coordinate
The registration result has total residual error 2.15.
GCP points were visually observed and extracted
the coordinate of its point from the input image;moreover, they are use in the calculation of image
matching as initial values (table 1). The selectionof initial values is an important task in the LSM
technique; adjusting initial condition the result canbe improved. The initial values were adjusted in
until the acceptable output.

Table 3: residual in registration (unit in pixel)

Table 1: initial value from visual selection (unit inpixel)

5 Conclusions

3.2 Least Squares Matching
Least squares matching method was used to selectGCP points. The selected points are represented inthe (table 2). It could be applied in the image
registration process.
Table 2: LSM method GCPs result (unit in pixel)

Based on the results comparison, we could conclude
that the land-covered reflectance spectra arerespectively affected to the satellite image especiallyon the flatland, forest, and build-up area. It could bemaintained by adding spectral values of ASTER
spectral library. On the other hand, elevated area andsome crop land were issued good response It says thatsimulated satellite images from elevation data could beapplied to register satellite images; moreover, it could
be improved by adding spectra reflectance value.

6 Future works
The future works:• will be investigated the selection of initialvalue• will be removed shadow and shade disturbancefrom high resolution satellite data
• will be added land surface reflectance spectra
to simulated image

References:

The result GCPs shows that there are some variations
between initial values and calculated values.

4 Result of Registration
Image registration was carryout using calculated GCPs.
The RMSE for a collection of N values x1, x2,…, x1 (x:residual) is:

Total RMS error

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