1.
KAMLESH KUMAR

2.
Raster data is commonly obtained by scanning maps or collecting aerial photographs and satellite images. Scanned map
datasets don't normally contain spatial reference information (either embedded in the file or as a separate file). With aerial
photography and satellite imagery, sometimes the location information delivered with them is inadequate, and the data
does not align properly with other data one has. Thus, to use some raster datasets in conjunction with other spatial data,
we need to align or georeference them to a map coordinate system. A map coordinate system is defined using a map projection (a
method by which the curved surface of the earth is portrayed on a flat surface). Georeferencing a raster data defines its
location using map coordinates and assigns the coordinate system of the data frame. Georeferencing raster data allows it
to be viewed, queried, and analyzed with other geographic data.
Generally, we georeference raster data using existing spatial data (target data)—such as georeferenced rasters or a
vector feature class—that resides in the desired map coordinate system. The process involves identifying a series of
ground control points—known x,y coordinates—that link locations on the raster dataset with locations in the spatially
referenced data (target data). Control points are locations that can be accurately identified on the raster dataset and in
real-world coordinates. Many different types of features can be used as identifiable locations, such as road or stream
intersections, the mouth of a stream, rock outcrops, the end of a jetty of land, the corner of an established field, street
corners, or the intersection of two hedgerows. The control points are used to build a polynomial transformation that
will shift the raster dataset from its existing location to the spatially correct location. The connection between one
control point on the raster dataset (the from point) and the corresponding control point on the aligned target data (the
to point) is a link.
Finally, the georeferenced raster file can be exported for further usage.
Source: ArcMap

3.
1. Run the app. Right click
on the image list on the
content tab to add an
image. Go to Open
Raster Layer. (Ctrl+O)
2. Browse the file and
load it. Load both the
images adjacent to each
other.
STEPS

4.
As you can see the resolution (1;1) of panchromatic image on the
right is better and crispier than the multi-spectral left one.

5.
Notice that the first image has no resolution and has not been georeferenced.

6.
3. Go to Multispectral
after the image has been
loaded and click on
Control Points.
4. Select Polynomial in Set
Geometric Model dialogue
box.

7.
5. Another window of Multipoint Geometric Correction would appear with a dialog box of techniques to choose from for
the process, select Image layer and click OKAY.
6. Set an image layer as reference layer for the process.
In this Case which would be the georeferenced
LANDSAT Image.

8.
7. Set the Polynomial
Model Properties to 1
in this case. And
minimize it for the
time being.

9.
8. Mark the GCP points and rectify them by clicking/ moving on the generate GCP icon.
Resample Icon

10.
9. After all the required points have been marked (at least 4 for Polynomial 1), click on the Resample icon
to the second last of the icon row on the top. Tick on the Ignore Zero in Stats. And export the image.

11.
10. Load the new
image on the
Landsat image which
was georeferenced
within the same 2D
view panel. Go to
Home and click
Swipe.
NOTE: You could save the reference
point and export the polynomial
settings for further usage.

12.
Move along the Swipe Transition
Extent bar to view the accuracy.

13.
The swift transition shows the accuracy of
the georeferencing.

14.
Similarly, Polynomial 2 can be set by increasing the GCPs
to at least 6 and changing the polynomial settings to 2.

15.
The difference in the colour
shading and representation
is due to the different
sensors.

16.
Signing off..