gis and remote sensing lab 5

8/7/2019 Gis And Remote Sensing Lab 5

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GIS & REMOTE SENSING

LABORATORY SESSION 5:

GEOMETRICAL CORRECTION

NAME : XXXXXXXXXXXXXXXXXXXXXXXX

MATRIC NO. : UK XXXXX

PROGRAM : XXXXXXX

DATE : 12 AUGUST XXXX


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1. Introduction

Geometrical correction is the process of converting a digital map from one coordinate system to

another by using a set of control points and transformation equations. This correction is important due

to the fact that raw digital image will be more likely to contain geometrical distortion that usually

caused by the platform (position, velocity, and orientation), the sensor (orientation, integration time,

and field of view) the Earth (geoid, ellipsoid, and relief), and the cartographic projection (ellipsoid

and cartographic). As a result, this image or map cannot be used for any working or study purposes.

There is several methods which being used for correcting image distortion such as control points,

equiarea transformation, similarity transformation, affine transformation, projective transformation,

topological transformation. However in this lab session, we used control points method for correcting

the distortion by employing some highlights features in the map such as the crossing road, building or

other static man made structure. There features are known as ground control points which must be

identified both in the image and in the reference map.

The coordinates of the ground control points in both image and map are used to produce a

mathematical model. This mathematical model is used to correct the distortion of image. The PCI

software solves equations that describe the relationship between the two coordinate system (using the

ground control points information) to produce two equations for the conversion of the X and Y

coordinates from the reference image to the new image.

Geometric correction procedure can be done using two main methods which are image-to-map or

image-to-image. In this experiment session, we used image-to-map geometrical correction. This

technique uses the information extracted from the master image to give correction for the image.

Image-to-image method corrects the geometric position by registering the (slave) non-geocoded

image to the (master) geocoded image.Image-to-map method is using the information from the map

in geometric correction. This information then becomes the master image in image-to-image method.

This transformation process is based on polynomial equation that split into three degree which is 1 st

order, 2nd order and 3rd order polynomial.


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1st order polynomial equation:

1

2nd order polynomial equation:

3rd order polynomial equation:

where ,

is a coordinate on the slave image

is a coordinate on the master image.

2. Objectives

In this experiment, PCI software was used to determine the proper procedure and method of how

satellite data or digital data being corrected its geometric position on the scale or to meet certain

projections.

3. Methodology

3.1 The Image Subset

1. Open the PCI Geomatica V10.0

2. Open file>Local Disk D > UK14859 > Click 12657-220697 > Zoom to overview


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3. Click tools > Clipping/subsetting > Click the box beside Rasters > Browse > UK14859 >Type the

name of the file (Uncorrected image 12-8) > Resize the image > Clip

3.2 Ground Control Point (GCP)

1. Click OrthoEngine in Geomatica Toolbar V10.0.0

Figure 2. Geomatica Toolbar V10.3.0 (Geomatica Toolbar

V10.0.0 for the laboratory session)

2. Open File > New > Uncorrected image 12-8

3.Set projection > Click Earth Model > ellipsoids > E012-WGS84 > accept >Output pixel >spacing 25m > Set GCP Projection based on Output Projection

4. Open GCP collection in OrthoEngine > Choose uncorrected image > open the subset image >

Uncorrected image 12-8 > Quick Open & Close icon

5. Collect GCP > Geocoded image > Sgkuantan_geo > Choose seven points in both picture > Value

polynomial order set to 2.

6. RMS error should be less than 0.5 > Save for re-sampling.

7. Geometric correction > Schedule Geometric Correction


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8. Resampling > Choose nearest neighbor > Correct the image > Display the image at Geometrica

focus

9. Open Reports > Click residual report icon

4. Image Results

Figures below show the seven points used in this experiment. For this image, the RGB channel 4, 5,

and 3 was used with linear enhancement. The image below (Uncorrected image 12-8) is non-

geocoded image while the second image (Sgkuantan_geo) is the geocoded image.


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Figure 2. Uncorrected image 12-8.pix (non-geocoded image)


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Figure 3. Sgkuantan_geo (geocoded image)


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In this experiment, 7 points were chosen which result in RMS error less than 0.5 with the value of

0.29. The XRMS obtained is 0.15 pixels and YRMS is 0.25 pixels. The polynomial order was set to

value 2 to reduce the RMS error. Figure below shows the point ID that was used.

Figure 4. GCP collection for uncorrected image


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The result below was produced after the geometric correction has been done.

Figure 5. Geometrically corrected image

5. DISCUSSION

The geometric image correction procedure was practiced during the laboratory session. This

geometric correction was done on raw image (acquired by earth observation) before it can be

transferred to maps due to geometrical distortion. Raw digital images contain geometric distortions

that make them unsatisfactory for use as maps. These geometric distortions can be caused by such

things as: the Earths curvature, atmospheric refraction and panoramic distortion. The goal of


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geometric correction is to correct for these distortions, in order to produce an image with the

geometric integrity of a map.

In this experiment, the difference between uncorrected and geo referenced images is an important.

The uncorrected image, also referred as an 'input', 'source', or 'slave' image, is the image that will be

to geocoded. The geo referenced image, often referred to as the 'target', 'output' or 'master' image, is

the image you wish to use as the 'correct' image. Once an image has been corrected using ground

control points (GCPs), it is said to be geocoded.

In this experiment, the geometric correction was done by selecting the Ground Control Point or GCP.

GCP is a specific pixel on an image or location on a map whose geographic coordinates are known.

Intersection of roads, building, bridge, and other man made structure was chosen as the GCP for

geometric correction procedure. Note that the natural structure cannot be used as GCP as it prone to

change over time. In this experiment, the positioning of the GCP is crucial to obtain good results. So I

choose GCPs which are clearly visible in each image and the seven chosen GCPs was evenly

distributed throughout the image.

During the image correction procedure, the Root Mean Square (RMS) error must be less than 0.5.

RMS error disguised accuracy of the transformation by comparing the actual location of the map

coordinate to the transformed position in the raster. The distance between these two points is known

as residual error. This value describes how consistent the transformation is between the different

control points.

For this experiment, I choose seven GCP points and used 2 nd order polynomial method for this

geometric correction procedure. As a result, the RMS error obtained was 0.29 pixels which not

exceed 0.5 pixels in order for the geometric correction to be accurate. Then, the resampling has been

done using the nearest neighbor method. Finally, the corrected image is produced, but the image was

slightly slanted compared to uncorrected image.


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6. CONCLUSION

There are several important issues that can be summarized for the geometric correction experiment

such as;

1. Every image taken from satellite sensor has geometric error.

2. Geometric correction was done to make the image geometrically positioned on the scale or to

meet certain projections.

3. Chosen GCPs point must be clearly visible in each image and the GCPs points must evenly

distributed throughout the image.

4. We must have more than minimum GCPs for each degree polynomial to prevent undefined

residual or error.

gis and remote sensing lab 5

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