Final Project
Digital Image Processing Research Paper
GEO 508 Digital Remote Sensing Dr. John Althausen Unsupervised Classification of
Spectrally
Enhanced Landsat TM Data of Midland, MI
Completed by Jesse S. Frankovich, December 1999
Contents Abstract
Background Information
Image Rectification
Spectral Enhancements
Unsupervised Classification
Accuracy Assessment
Conclusions/References | C
C
C
A
A
Images of the Rectification Process
Spectrally Enhanced Images
Land Cover Map/Image Comparison |
Note: The images listed on the right can also be viewed via links within the
corresponding section of the body of the paper. |
Abstract This goal of this project was to produce a land cover classification map of the
Midland, Michigan area from a subset of a Landsat Thematic Mapper (TM) multispectral
image. The map was created using unsupervised classification techniques in ERDAS
IMAGINE, a digital image processing software package. Rather than simply use the TM
bands in their original form, I first performed two spectral enhancements on the data,
namely, principal components analysis and the tasseled cap transformation. These
enhancements were done in an attempt to decrease both information redundancy and the
number of layers used for the unsupervised classification. The new layers were then
merged into one image from which the computer generated 60 clusters of similar pixels.
Next, I classified each cluster into one of six land cover categories. Since the main
objective of this project was to experiment with spectral enhancements and find out if they
could be used to create a good classification map, an accuracy assessment was performed
to determine if the spectrally enhanced data resulted in a satisfactory product. |
Background Information Midland, Michigan has been my hometown since 1991. The
image below shows the Midland area with some of the main features labeled. The most
pronounced feature in the area is the large industrial district occupied by Dow Chemical
and Dow Corning Corporations. This includes parking lots, manufacturing plants, and
other buildings, plus several man-made bodies of water for industrial waste treatment.
Aside from this, Midland is a typical town without about 40,000 residents. There are two
main business districts, and the residential areas are characterized by having streets lined
with numerous trees. The city is surrounded by a mix of hardwood forests and agricultural
lands. The particular Landsat TM image which I used for this project was acquired in June;
thus, the agricultural fields are almost all still bare. Highways US-10 and M-47 are two of
the major transportation routes on the image, and the Tittabawassee River winds its way
from the northwest to the southeast. One other prominent feature outside of town is the
plot of land that is home to the Dow Corning Corporation World Headquarters, most of
which is covered by a large expanse of lawn. Also visible in the image is the Midland
Country Club and a small part of Currie Municipal Golf Course. Rectified Subset of a
Landsat TM Image, June 13, 1988. Bands 3,2,1 (RGB).
Text and line features added using Macromedia Flash. |
Step One: Image Rectification Image rectification is an important procedure for many
image processing applications. Simply put, it is the process of converting a raw image into
a specified map projection (Sabins 266). The procedure involves the selection of
distinguishable ground control points (GCPs) in the image, such as road intersections.
These points are then assigned the appropriate reference information, such as
latitude/longitude or UTM coordinates. This reference data can be obtained from existing
map sheets or from fieldwork utilizing global positioning systems (GPS) (Sabins 266).
After a certain number of GCPs have been entered and referenced, the computer program
resamples the original pixels into the desired projection. The importance of rectification is
that the image can now be used in conjunction with other data sets. For example, the
rectified image could be opened in a Geographic Information Systems (GIS) program such
as ArcView. Since the image is now in a certain map projection, it should line up perfectly
with other projected layers of data, such as political boundaries, land use, soil types, road
networks, drainage systems, etc. If the image was collected recently, the information could
be used to update outdated GIS layers. The rectified image could also be used as the
reference source for image-to-image registration. For this project, I used a Landsat-5
Thematic Mapper (TM) image of Michigan. Click here for more information about
Landsat TM imagery. The characteristics of the TM image I used are summarized at the
end of this section. After acquiring the image from Dr. John Althausen, I selected a subset
image that included only the area (Midland) and the spectral bands I desired. Thematic
Mapper images have seven bands of data, but I chose not to keep the thermal band (Band
6) because it is not particularly useful for classification. The next step was to find
approximately 20 point locations such that I was able to 1) determine the Universal
Transverse Mercator (UTM) coordinates of each point using a USGS topographic quad
sheet, and 2) clearly see the point (such as a road intersection) on the image itself. I used
the Midland North and Midland South quad sheets and a transparent UTM grid to
determine the UTM coordinates (accurate to tens of meters) of each of the twenty points.
Then I was ready to begin the rectification in ERDAS. After specifying the desired map
projection information, I used the appropriate tools to: 1. Zoom in on the area of the
image containing a Ground Control Point (GCP). 2. Click on the GCP location to
acquire the x and y input coordinates. 3. Type in the corresponding UTM reference
