1. A PYTHON SCRIPT TO PAN-SHARPEN IMAGERY
& SUBSET IT WITH A QUARTERQUAD SHAPEFILE
FINAL PROJECT
CHRIS CURLIS
GEOGRAPHY 376
AMERICAN RIVER COLLEGE
SPRING 2010
NATHAN JENNINGS, INSTRUCTOR
MAY 14, 2010

2. The goal of this project was to create a python script which
would pan-sharpen a Landsat 7 image and subset the new
sharpened imagery by quarterquad.
A shapefile was first created of those quarterquads which
were of interest because they had no gaps from the
SLC malfunction.
The final products are:
A full pan-sharpened Landsat 7 TM scene
A set of shapefile boundaries for each quarterquad of interest
A set of pan-sharpened TM quarterquad images

3. Due to scan line corrector malfunction, imagery has
LANDSAT 7 IMAGERY
Test
Due to scan line corrector
malfunction, imagery has
gaps on either side but there
is a path in the middle of
the image with no gaps.

4. Statewide grid of quarterquads overlaying
Landsat 7 TM imagery.

5. Close up of gap and gap free area in image

6. A statewide quarterquad shapefile was used
To identify which quarterquads have no gaps

7. A statewide quarterquad shapefile was used
To identify which quarterquads have no gaps
This shapefile(NoGaps.shp)
was created by visually
selecting q-quads with
no gaps in imagery.

8. Arc Toolbox has a
tool for creating
pan-sharpened imagery
using coarser (30 meter)
multispectral imagery with
finer (15 meter) panchromatic
imagery
The toolbox was used
to study and generate
a python script example
of pan-sharpening

9. The parameters needed for pan-sharpening.
Greatest weight is given to the infrared band of imagery.
The weights can be set or defaults used based on pan-sharpening type.

10. The python script checks for a pre-existing pan-sharpened image and
deletes it.
The variables are from modelbuilder exported script.
The script creates a new pan-sharpened image of the full Landsat 7 scene.

11. Next a search cursor is created that loops through all the records in the
table for NoGaps.shp
A portion of the script checks for the existence of the entire “Outputs” directory
which has over 100 files and deletes it. a new “Outputs” directory is then created.

12. Two of the fields in the NoGaps.shp table are combined to name
the mask shapefiles and the subset pan-sharpened images:
“USGSID” and “Qdrnt” since “QQNAME” has spaces in it.

13. This “try” statement of the script begins looping through the
FID values using “while” and gives a name combining “USGSID” and
“Qdrnt”. The selected quarterquads become new individual shapefiles
and these shapefiles are used with “extract by mask” on the pan-sharpened
Landsat 7 TM scene to create individual quarterquad images.
Using row = rows.Next(), the script continues looping until all FID’s have been
selected and new files created.
The script then prints: “Script completed…all quarterquads have been clipped”

14. The script begins:

15. The script finishes:

16. 30 meter resolution multispectral Landsat imagery

17. 15 meter resolution panchromatic Landsat imagery

18. 15 meter resolution pan-sharpened imagery quarterquad overlaying 30 meter Landsat imagery
SUCCESS!