Unsupervised Land Cover Classification
An introduction to digital image classification using the Multispectral Image Data Analysis
System (MultiSpec©)
Level: Grades 6 to 9
Macintosh version
With Teacher Notes
Earth Observation Day Tutorial Series
Developed by The University of Georgia Chapter of the American Society for Photogrammetry and
Remote Sensing (ASPRS)
Unsupervised Land Cover Classification – Earth Observation Day Tutorial Series
An introduction to digital image classification using MultiSpec© for Macintosh computers
Purpose
Other Concepts
To display, classify and verify the quality of the
land cover classification of a section of a satellite
image, using the Multispectral Image Data Analysis
System (MultiSpec©) software and an
unsupervised classification method.
Mathematics and Computing
Math concepts are reinforced, including mean,
range and variability.
Student Outcomes
Students gain experience in the use of remotely
sensed image data. Students learn how to use an
image processing tool to classify a remotely sensed
image. Students gain/improve spatial or landscape
level perspective of the area around their school.
Science Concepts
Geography
The characteristics and spatial distribution of
land cover types and ground elements
surrounding the students’ school.
How humans change their environment and how
the environment changes human patterns of
land use.
Physics
Energy-object interaction and the use of remote
sensors to detect the energy reflected from
objects.
Color formation and color systems.
Life Sciences
The characteristics of vegetation communities
surrounding the students’ school.
The distribution of habitat types surrounding the
students’ school.
Scientific Inquiry Abilities
Identify and classify land cover.
Assess the quality of the classification results.
Identify answerable questions.
Conduct scientific investigations.
Develop descriptions using evidence.
Communicate results.
Use of algorithms for digital image analysis.
Specific Learning Objectives
Upon successful completion of this lesson students
will be able to:
Inspect a Landsat-TM image to identify land
cover types and ground elements.
Classify a Landsat-TM image using an
unsupervised classification method.
Verify the accuracy of a land cover classification
using reference data.
National Science Education Standards
Content Standard A: Science as Inquiry
Abilities necessary to do scientific inquiry
Understanding about scientific energy
Content Standard D: Earth and Space Science
Standards
Structure of the earth system
Content Standard E: Science and Technology
Standards
Abilities of technological design
Understanding about science and technology
Content Standard F: Science in Personal and
Social Perspectives
Populations, resources, and environments
Science and technology in society
National Educational Technology
Standards (NETS-S)
Creativity and Innovation
Apply existing knowledge to generate new ideas,
products, or processes
Identify trends and forecast possibilities
Research and Information Fluency
Locate, organize, analyze, evaluate, synthesize,
and ethically use information from a variety of
sources and media
Evaluate and select information sources and
digital tools based on the appropriateness to
specific tasks
Process data and report results
Critical Thinking, Problem Solving, and Decision
Making
Identify and define authentic problems and
significant questions for investigation
Collect and analyze data to identify solutions
and/or make informed decisions
Use multiple processes and diverse perspectives
to explore alternative solutions
Level
Grades 6 to 9
Time
Two sessions of 60 minutes.
Session 1: image classification.
Session 2: classification quality analysis and
accuracy assessment.
Time includes the presentation of the activity and
background information, as well as hands-on
activities.
Materials
Computer(s) running Macintosh operating
system (if students will be using Windows
computers, please refer to the PC version of this
tutorial). Note: we suggest students work as a
group, with ideally two students per computer.
MultiSpec© software: this tutorial was created
using MultiSpec© Universal version for
Macintosh computers.
Pre-selected section of a remotely sensed image
acquired by the Thematic Mapper (TM) sensor
onboard the Landsat satellite.
Color printer (optional).
The classification accuracy assessment activity
requires students to have a calculator or access
to spreadsheet software.
Preparation
This lesson is designed to follow the “Viewing
Remote Sensing Imagery with MultiSpec©”
tutorial, also from AmericaView. Before beginning
this lesson, students should be familiar with
opening and visualizing a satellite image using
MultiSpec© software. See the accompanying
Teacher Preparation Plan document for details.
After completion of the “Viewing remote sensing
Imagery” tutorial, MultiSpec© should be installed
on each computer to be used by the students, and
you should be familiar with the software.
If some time has passed since you performed the
“Viewing...” tutorial, you may wish to take some
time to re-familiarize yourself and students with
MultiSpec©.
The Landsat-TM image previously downloaded and
clipped for the “Vieweing...” tutorial may also be
used for this tutorial.
Preparation (cont.)
If you no longer have access to the Landsat-TM
image used for the “Viewing...”tutorial, follow
the instructions in the teacher Preparation Plan
to download the Landsat-TM image, identify the
area to be classified and clip a subset of the
image representing the area to be classified.
Save copies of the selected section of the
Landsat-TM image on the computers the
students will be using.
Make sure students will have read and write
permissions to access the necessary folders,
when following the tutorial.
Identify land cover types in the selected subsection of the Landsat-TM image.
Make sure students will have access to the
United States Geological Survey (USGS)
EarthExplorer website through a web browser.
If a printer will be used (optional) verify that it
can be accessed by every computer used in this
exercise and that the printer has ink.
Make sure students will have access to a
calculator or a spreadsheet during the quality
assessment analysis.
Preparation Time
If the teacher has not used MultiSpec© before,
he/she should plan on approximately one hour
to become familiar with the software.
Image preparation time: if the image to be used
in the tutorial is part of USGS’s image archive
(i.e., the image covering your area has already
been processed and is available for download),
preparation time may take around one hour. In
this case, image preparation would include
image download, area selection and the
cropping of an image section. If the image
covering your area is not available for immediate
download, you need to request USGS to process
this image. Normally, you will receive a
notification from USGS that the image is
available for download two to three days after
you place the image processing request.
MultiSpec© software and data copy time (setup
time): MultiSpec© and the section of the
Landsat-TM image must be available on every
computer to be used by this exercise. Setup time
per computer is less than five minutes if a
computer account with read/write permissions
already exists (make sure students will be able
to read from/write to the folder where the
section of the Landsat-TM image is stored).
The teacher should also consider allocating time
to understand the basics of remote sensing,
including image acquisition by remote sensors
and the characteristics and use of the acquired
images.
This lesson may be presented in conjunction
with the other AmericaView Earth Observation
Day MultiSpec© tutorials. These tutorials guide
students to classify the land cover around their
schools through supervised classification
methods and to conduct a land cover change
analysis. For more information on these
tutorials, see the AmericaView Earth
Observation Day website
http://earthobservationday.com
Lesson Overview
This lesson will guide students in the process of classifying a section of a Landsat-Thematic Mapper
(Landsat-TM) image into multiple land cover classes. For that, students will use the MultiSpec© image
processing software and an unsupervised image classification method. The classification process will be
followed by the verification of the classification quality and an accuracy assessment. The main steps
involved in this lesson are:




Display and inspect a section of an image acquired by the Landsat-TM satellite
Classify a section of the Landsat-TM image using an unsupervised classification method
Display and inspect the classification results
Analyze results and verify the quality of the classification, through a classification accuracy
assessment
Suggested Sequence of Activities







Present an overview of the exercise
Present background information regarding remote sensing: the Landsat satellite, spatial resolution
(pixel size), spectral resolution (bands), spectral response of selected land cover types, including
vegetation, soil, water and constructed areas
Present the basics of unsupervised classification and the ISODATA classification method
Inform students on where the image to be classified (tmimage.tif) is located and where to save the
results of the analyses
Conduct image analysis: image display and classification
Present the students the basics of classification accuracy assessment
Conduct classification results analysis through the inspection of results and classification accuracy
assessment.
Frequently Asked Questions
1. Why are the colors for the same land cover
types so different when I compare my image
with the images in the “Earth Observation Day
Powerpoint” presentation?
Some difference in color between images is
normal. If the color difference is considerable and
all other elements of your screen seem to present
their normal colors, you may have loaded your
image using a color composite different from the
suggested color-channel association. Reload the
image and in the Set Display Specification dialog
box make sure you have 3-Channel Color for Type
and the values 5, 4 and 3 associated with the Red,
Green and Blue Channels.
2. How many clusters should I use?
We recommend using seven clusters, which should
be enough to differentiate the main land cover
types in your area. You can increase this number if
multiple land cover classes are being grouped into
a single cluster.
3. I have used MultiSpec© to create seven
clusters. In one of the clusters I know there are
two different types of land cover classes. What
do I do?
You can repeat the classification, increasing the
number of clusters, for example, to ten. It is also
possible that the spectral patterns of the two land
cover types are close enough that the software
cannot distinguish them.
Frequently Asked Questions (cont.)
4. I cannot differentiate roads from commercial
areas. What should I do?
Developed areas may be difficult to differentiate in
a satellite image. All are composed of minerals and
often have very similar reflectance patterns. It may
not be possible to separate them when clustering.
Sometimes, you can create a separate sub-image
of just the urban area and cluster that alone in
order to differentiate the different urban types.
5. What do we do if there is an area in our image
for which nobody knows the land cover
Remote sensing is a powerful tool, but it may be
the case that remote sensing alone cannot provide
all the information necessary to classify certain
land cover types. Projects incorporating remote
sensing usually include field work. During field
activities, the area being analyzed is visited and
data are gathered to support information
extraction and land cover classification using
images.
6. Why do I see errors in the classification and
what can I do to increase classification quality?
Sometimes, the use of multispectral images and
unsupervised classification methods may not be
enough to classify the high variability and
complexity of some natural and man-made
systems. In some cases, you may be able to
increase the quality of your classification by
changing cluster classification settings and running
the
classification
again.
Depending
on
classification quality requirements and the ground
elements being analyzed, data from specific
sensors and with specific characteristics may be
required. A different classification method may
also be necessary.
as those factors associated with the classification
method and settings chosen.
8. When analyzing classification quality, what land
cover type should I choose, if the reference
image is showing multiple land cover types
around the coordinates of a classified point?
Your reference image on the USGS EarthExplorer
website will probably have finer Therefore, it is
expected that EarthExplorer will have multiple
ground elements (represented by multiple pixels)
inside a single Landsat-TM pixel. In most cases you
can choose the predominant land cover type
around a given coordinate when evaluating the
results of image classification. For example,
coordinates from the classified image may direct
you to a gap in a forest, where you may be able to
see bare soil and shadow, surrounded by trees. If
the gap is not very large, the predominant land
cover type contributing to the final Landsat-TM
pixel value may still be forest.
9. The location of ground objects in the
MultiSpec© image does not seem to match the
location of the same objects in the USGS
EarthExplorer image.
Remote sensing products need to be geometrically
adjusted, so coordinates can be used for image
navigation and feature location. These geometrical
adjustments may not be exact and positional
discrepancies may occur. If these differences seem
to be consistent throughout your image section,
you can consider the difference in position of a
selected point (image offset) when locating
objects. We suggest using a color composite of the
Landsat-TM image and locating features not only
by using coordinates, but also by locating other
ground elements in the area (for example, roads,
7. What factors affect the quality of the building and streams) and considering their
classification?
relative position to the features analyzed.
The list of factors affecting the quality of your
classification is long and depends on the
complexity of your ground elements and study
area; your input data (which are, for instance,
affected by the atmosphere, acquisition geometry,
shadows, sensor failure and image noise); as well
Frequently Asked Questions (cont.)
10. Where can I find more information about
remote sensing and digital image processing?
For an introductory approach, we suggest the
following books:
 Remote Sensing of the Environment: An Earth
Resource Perspective, by John R. Jensen.
 Remote Sensing and Image Interpretation, by
Thomas
Lillesand,
Ralph
W.
Kiefer
and Jonathan Chipman. 2007
Unsupervised Land Cover Classification – Earth Observation Day Tutorial Series
An introduction to digital image classification using MultiSpec© for Macintosh computers
Lesson Overview
In this exercise, you will classify a section of a Landsat-Thematic Mapper (Landsat-TM) satellite image
showing your school and surroundings. You will use the MultiSpec© image processing software and an
unsupervised classification method to automatically classify this image into multiple land cover types
occurring in the area. The classification process will be followed by image inspection and the verification
of the classification quality. The main steps involved in this lesson are:




Display a section of an image acquired by the Landsat-TM orbital sensor
Classify the Landsat-TM image using an unsupervised classification method
Display and inspect the classification results
Analyze results and verify the quality of the classification.
Classroom Instructions:
1) Start by launching MultiSpec© using the icon on the desktop.
Note to Teacher: Please refer to the “MultiSpec© Teacher Tutorial…” preparation guide of this
Tutorial Series for instructions on how to create a desktop icon to launch MultiSpec©.
2) Load a Landsat-TM image into MultiSpec© in the same way you loaded the image in the
“Viewing Remote Sensing Imagery with MultiSpec©” Tutorial. From MultiSpec©’s File menu,
choose Open Image …. An Open dialog box will be displayed, allowing you to select the image
file to be loaded.
Note to Teacher: A detailed description on loading and viewing remote sensing imagery is
provided in the “Viewing Remote Sensing Imagery with MultiSpec©” tutorial, available from
AmericaView. You may refer the students to this tutorial if you feel they need a refresher on
how to load and view these images.
3) Using the Open dialog box, navigate to the folder where the tmimage.tif file is stored (The
teacher will provide instructions on where to find this file). Select the tmimage.tif image and
click Open. The Multispectral Display Specifications dialog box will open (Figure 1).
4) In the Display group of the Multispectral Display Specifications dialog box, make sure you have
3-Channel Color for Type and the values 5, 4 and 3 associated with the Red, Green and Blue
Channels. Enter the value 2 for Magnification. Click Ok. If this is the first time this image is
displayed, the Set Histogram Specification dialog box will open.
Figure 1: Multispectral Display Specifications dialog box for loading the tmimage.tif image. Your
values for Line and Column in the Area to Display group may differ from the ones presented in the
figure.
5) In the Set Histogram Specifications dialog box, accept the default options by pressing OK. The
tmimage.tif image will load. Remember that the Text Output window will now be updated with
statistics referring to the values associated with each pixel of an image (the digital numbers).
These numbers will be used by MultiSpec© to display the image in the resulting window. The
numbers are stored in a .sta file so that they will not need to be computed again.
Note to Teacher: Image statistics are calculated and presented for each spectral band and
reflect the average, range and variability of digital numbers in the band. One can use the
Selection Graph feature of MultiSpec© for a more detailed numerical analysis of the digital
numbers in the image and specific land cover types and/or ground elements.
6) Visually inspect the displayed image and think about how pixels in the image might be classified
into categories of differing land cover types. How many different land cover types can you
identify here? Do you see roads and constructed areas? What about vegetation and water?
What other features can be identified in the image?
Note to Teacher: The following steps will deal with land cover classification of the Landsat-TM
image section. Land cover classes that may be present in your image are: bare soil, vegetation,
constructed areas, water and shadows. Some of these classes can be subdivided and detailed
(for instance, light soil, dark soil and dense vegetation, sparse vegetation).
We suggest preceding the following steps of the lesson with a review of the “Earth Observation
Day” Powerpoint presentation showing examples of how land cover types and ground elements
are depicted by color composites of images acquired by the Landsat-TM sensor.
Note to Student: The colors displayed by the loaded image section depend on how we choose
to load an image on the computer monitor. When we loaded the tmimage.tif image, we chose
to associate band 5 of the Landsat image with the color red in your computer’s monitor, band 4
with the color green, and band 3 with the color blue. Different associations between bands and
displayed colors will produce different visual results. Depending on the purpose of the
classification, this can be desirable.
7) We will now classify the tmimage.tif image by grouping similar pixels into clusters or categories.
This method of classification is “unsupervised”, meaning that the person using the computer
does not have to inform MultiSpec© on what types of land cover exist in the area and where
they are found before they begin classifying. Instead, MultiSpec© groups pixels into clusters
based on how similar their band values are to each other. To prepare MultiSpec© for the
classification, from the Processor menu, select Cluster. The Set Cluster Specifications dialog box
will open.
8) In the Set Cluster Specifications dialog box, select ISODATA as Algorithm. The Set ISODATA
Cluster Specifications dialog box will open.
9) In the Set ISODATA Cluster Specification dialog box, under the Other Options group, enter 7 for
the number of clusters and 100 for the convergence percentage. Make sure the line intervals
and the column intervals are set to 1. Select OK.
Note to Teacher: The use of seven clusters should allow the ISODATA algorithm to discriminate
the land cover types in your area. If necessary, you can instruct your students to increase the
number of clusters, which will increase the number of groups of digital numbers and potentially
give more detail and control over the classification results. Notice that the choice of seven
clusters only informs the classification algorithm to try to separate the image into seven groups.
The final number of clusters in the resulting image depends on the digital numbers stored in the
image.
The ISODATA classification is an iterative (multiple-run) process. For each iteration, pixels may
join or leave clusters, depending on the similarity between these pixels and the statistics of the
cluster (note that when a pixel joins or leaves a cluster, the cluster statistics change). The
convergence percentage is the percentage of pixels that do not change clusters from one
iteration to the next. Our choice of 100% forces our classification process to run until no pixel
changes clusters. If experimenting with larger images, a 100% convergence percentage may
increase the classification time considerably. Because we are classifying a relatively small image
section, using the 100% value should not impact the time it takes to complete the cluster
classification.
10) Back in the Set Cluster Specifications dialog box (Figure 2), click on the Cluster Stats drop-down
menu and select Do Not Save. Under the Write Cluster Report/Map To group, check Cluster
mask file and Image window overlay. Using these options, the clustering process will create a
classified image and save it to the computer. The image will also be displayed as an overlay on
the multispectral image window (Figure 2 shows the Set Cluster Specifications dialog box with
the classification parameters you should use). After entering these parameters, select OK to
close this dialog box.
Figure 2: Set Cluster Specifications dialog box and selected classification parameters.
11) You will be prompted to enter a name for the cluster map file and where to save this file. Use
the default settings by selecting OK in the Save As dialog box. The unsupervised classification
using the ISODATA method of clustering will start and a window will be displayed showing the
progress of the classification process.
12) The unsupervised classification will create an image with around 7 classes, which will be
displayed and saved to the computer. Notice also that the Text Output window will present
statistics associated with the classification. These values, including the mean values for each
image band for each cluster, can be consulted when analyzing how the original pixel values of
the multispectral image were mapped to individual clusters. As an example, Figure 3 shows a
cluster map overlay resulting from a classification of a section of an image of Athens, Georgia.
Note to Student: Your classification results may display differently, as these results depend on
the distribution of land cover classes around your school.
Figure 3: Sample unsupervised (cluster) classification result of a Landsat-TM multispectral image of a
portion of Athens, Georgia.
13) Experiment toggling the overlay off and on a few times and inspecting how individual clusters
relate to the color composite image. To turn the overlay off, click on the red O button (
) and
select No overlays. To turn the overlay back on, click the red O button and select the
tmi_Clus_ID_7 option. After you finish analyzing the results, turn the overlay off.
14) Now, open the cluster map image. This will be the same classification result you inspected as an
overlay, but you will have more control over the cluster classes. From the File menu, select
Open Image to bring up the Open image dialog box. You may have to change the Files of Type
drop-down menu to Thematic (*.gis; *.tif; *.clu). Then select tmimage_clMask.gis and click
Open. The Set Thematic Display Specifications dialog box will open. Press OK, as the default
settings are fine. A thematic image will be displayed.
15) You can control the magnification of the thematic image by using the
and
buttons in
the menu bar. Use the Command key with the mountain icons to adjust the zoom step factor
and the magnification. Also, resize the tmimage_clMask.gis window by dragging its border.
Arrange your windows side by side, to resemble Figure 4, below.
Figure 4: Color composite of a Landsat-TM sensor image (left) and image classification result (right).
Images are arranged side-by-side for comparison.
16) Note that the image classification result window (right image) is divided into two parts. On the
left we have the legend for the cluster classification. The image with the result of our
classification is to the right of the legend. Compare the classification results with the original
image window. Note how areas of bare soil, vegetation, constructed areas, water and shadows
were classified (Depending on the area represented by your image, you may not have all these
ground elements present in the scene).
17) Now it is time to inspect your results and identify your clusters. There are several things you can
do to make this task easier:
a. In the legend, you can move the cursor over a color chip associated with a cluster, hold
the Shift key down (the cursor will change to an open eye) and click the left mouse
button down and up to cause the colors for that class to blink off and on, changing to
white and back to the class color.
b. If you hold down both the Shift and Control keys and then click the left mouse button
down and up, then all of the other classes will blink off and on.
c. Change the cluster class names by double clicking on the name to the right of the color
chip. An Edit Thematic Class Name dialog box will open. To change the name of the
cluster/class, enter the new name and press OK. Can you use some of the ground
elements you identified when you filled out TABLE 1 from the “Viewing...”tutorial to
name these clusters?
d. Also change the class color by double clicking on the color chip. A Color selector window
will open. Select colors that suggest the type of land cover class you are representing
(for instance, green for vegetation, brown for bare soil, blue for water – you can be
creative here). Select the new color and press OK.
e. To save your class changes, go to the File menu and select the Save Thematic Class Info
As option (this option is only visible when Classes is selected in the drop-down menu of
the tmimage_clMask.gis window). The Save Thematic Class Information As: dialog
window will open. Press Save.
f.
Finally, if your inspection showed that more than one of your clusters belong to the
same land cover class (for instance, two of your clusters represent water), group these
clusters together into “information groups” by selecting Groups/Classes in the dropdown menu above the legend. To do that, click on the class name (class names start
with a number) and drag the class into similar cluster groups. You can double click on
the cluster group name to change the name of the group. Select the Classes option in
the drop-down menu above the legend to display the original cluster classes.
g. To save your Groups/Classes changes, go to the File menu and select the Save Thematic
Group Info option (This option is only visible when Groups/Classes or Groups is selected
in the drop-down menu of the tmimage_clMask.gis window.).
18) Close the MultiSpec© software by selecting MultiSpec menu and then Quit MultiSpec.
MultiSpec© will alert you in case there is any unsaved change to your classification product and
will allow you to save those changes.
19) Congratulations!! You just finished the image visualization and unsupervised classification
tutorial, using a Landsat-TM image and the MultiSpec© image analysis software.
Note to Teacher: If a printer is available, following the inspection and editing of the classified image
students may print their classification results, ideally in color. To print the classification results, give the
following instructions to your students:
a. Click anywhere inside the classified image window or on the window border. The classified
image window receives focus.
b. Go to the File menu and select Print Image. In the Print window, click Preview. The classified
image window then shows how the image will be printed (in case you change your mind and
decide not to send your results to the printer, press the Cancel button on the print preview
window and you will be taken back to the classified image).
c. If the print preview looks ok, press the Print button on the classified image window to print your
results.
Note to Teacher: This is the suggested end of the first session of this activity, involving image
visualization and classification. An extension of this activity is proposed below, which can be developed
with your students at a second session. This extension session will involve evaluating the accuracy of the
land cover classification resulting from the first session.
Unsupervised Land Cover Classification – Earth Observation Day Tutorial Series
Accuracy Assessment
An introduction to digital image classification using MultiSpec© for Macintosh computers
Note to Teacher: This activity is a suggested extension of the Unsupervised Land Cover Classification
exercise above. The following steps guide students in the evaluation of the quality of their classified
product and include accuracy assessment procedures.
Specific requirements associated with this activity include the per student/group of students availability
of :
 a section of a Landsat-TM image (tmimage.tif file)
 a thematic image (tmimage_clMask.gis) representing the land cover classification of the
tmimage.tif file
 a computer with the MultiSpec© image processing software installed
 an internet connection and web browser
 a calculator or spreadsheet program
Background
Although automatic classification algorithms do a great job grouping remote sensing satellite image data
into clusters and classes, pixels can be misclassified or inappropriately grouped. This reduces the quality
of the classification results and can impact products and decisions based on image processing and
analysis. In order to better use products derived from remote sensing, it is necessary to understand and
communicate their quality. This exercise will guide you through steps associated with quality analysis
and accuracy assessment of your classification results.
Note to Teacher: Explain to your students that a detailed accuracy assessment, including an adequate
definition of samples and the verification of the necessary number of sampled points may take more
time and resources than the group has available. For instance, accuracy assessment procedures often
require that the points to be verified are chosen randomly and that sampled points are visited in the
field.
The current activity will teach the students principles associated with accuracy assessment. The
students will use a subset of the classified points to perform image verification by comparing the
classification results of these points with high-resolution imagery. )This approach is considered a proxy
for field work.).
Lesson Overview
We are going to evaluate the quality of our unsupervised classification by selecting classified points and
by visually identifying their predominant land cover class. To do that, we will use high spatial resolution
images as a reference. We will then use worksheets to record how well points were classified and will
later calculate the accuracy of our classification.
Classroom Instructions:
1) If it is not already open, launch MultiSpec© by using the icon in the desktop.
2) We will load into MultiSpec© the same Landsat-TM image we used during the unsupervised
classification procedure. From MultiSpec©’s File menu, choose Open Image …. An Open dialog
box will be displayed, allowing you to select the image file to be loaded.
3) Using the Open dialog box, navigate to the folder where the tmimage.tif file is stored. Select the
tmimage.tif image section and click Open. The Multispectral Display Specifications dialog box
will open.
4) In the Display group of the Multispectral Display Specifications dialog box, make sure you have
3-Channel Color for Type and the values 5, 4 and 3 associated with the Red, Green and Blue
Channels. Enter the value 2 for Magnification. Click Ok. If the Set Histogram Specifications
dialog box opens, follow step 5, other wise go to step 6.
5) In the Set Histogram Specifications dialog box, accept the default options by pressing OK.
6) After the tmimage.tif image loads, drag the border of the image window to enlarge it.
7) To be able to read latitude and longitude coordinates from the classified image, you need to
know the Landsat-TM image map parameters. We will read these parameters from the original
Landsat-TM image and apply then to the classified image. Click anywhere inside the displayed
tmimage.tif image and, from MultiSpec©’s Edit menu, choose Image Map Parameters … The
Set Map Coordinate Specifications window will open.
Figure 5: Set Map Coordinate Specifications
8) Use the space below to write down the parameters provided in the Grid Coordinate System and
Zone options, presented by the Set Map Coordinate Specifications window. Click OK when you
are done.
Grid Coordinate System: __________________________ Zone: ________________
Note to Teacher: Landsat-TM images downloaded from the United States Geological Survey
(USGS) use the Universal Transverse Mercator (UTM) projection and are therefore projected
images not geographic coordinate system images. This projection uses coordinates that are in
meters not geographic (latitude and longitude) coordinates. The map parameters above are
necessary, so MultiSpec© can convert from UTM coordinates to the geographic coordinates you
will need to complete this exercise.
9) Then, display the results of the unsupervised classification you conducted during the previous
session of this exercise (tmimage_clMask.gis file). From the File menu, select Open Image to
bring up the Open image dialog box. You may have to change the Files of Type drop-down menu
to Thematic (*.gis; *.tif; *.clu). Then select tmimage_clMask.gis and click Open. The Set
Thematic Display Specifications dialog box will open. The default settings are fine, so press OK.
The thematic image will be displayed.
10) After the tmimage.tif image loads, drag the border of the image window to enlarge it.
11) Now, we will apply the map parameters you retrieved before (from the Grid Coordinate System
and Zone options) to the classified image. Click anywhere inside the displayed classified image
to make sure it is the active window and, from MultiSpec©’s Edit menu, choose Image Map
Parameters … The Set Map Coordinate Specifications window will open.
12) Enter the parameters you wrote down to inform MultiSpec© what grid coordinate system and
zone to use with the classified image. After you enter the parameters, press OK to apply the
changes and close this window.
Note to Student: The changes to map parameters you just applied will be lost if you close the
classified image. If that happens, you can reopen the classified image and repeat the procedures
described above to reapply the parameters. Or save the image as a geotiff file using the
Processor->Reformat-Change Image File Format menu item.
13) To enable the presentation of latitudes and longitudes for individual pixels, click anywhere
inside the classified image window and go to the Window menu. Make sure the Show
Coordinates View option is selected. This will display the coordinate view within the window
just above the image.
14) Use the drop-down menu on the top of the classification display window to select the Lat-Long
(DMS) option. Move your cursor over the image to see the coordinates change (coordinates are
displayed to the right of the drop-down menu). These coordinates (latitude and longitude) will
help you find classified points in the reference image.
Note to Teacher: Before starting this activity, make sure your students will have Internet access
and that the EarthExplorer website is not blocked and can be accessed by all computers used.
15) Launch a web browser and go to http://earthexplorer.usgs.gov/. To visually identify the land
cover of a given area, we will use the image display and navigation features of the United States
Geological Survey (USGS) EarthExplorer website.
16) Using EarthExplorer (Figure 6), zoom in to the region you classified and get yourself acquainted
with the tools available and the ways you can interact with the website. Examples of things you
can do include:
a. To the left of the image panel there’s a text field where you can enter and locate an
address. When you enter an address and press Show, a marker indicating the address is
added to the image panel.
b. You can also use the Add Coordinate button to the left of the image panel to directly
enter latitude and longitude coordinates into EarthExplorer. A marker is added to the
image panel indicating the location associated with the coordinates entered.
c. On the image panel (right side of the screen) you can zoom in and out by using the
vertical controls (the plus and minus signs located at the extremities of the vertical line).
d. You can also hold down the left mouse button to pan the image.
e. Important: notice that as you move the cursor over the image, the coordinates on the
upper part of the image panel are updated. You can use these coordinates to locate
sample pixels and compare your classification results with your visual interpretation of
the land cover based on the image shown by EarthExplorer.
Figure 6: The United States Geological Survey EarthExplorer website.
Note to Teacher: The following step involves the writing down of geographic coordinates of
selected points, as well as image navigation based on these coordinates. We suggest preceding
this part of the activity with a description of the geographic coordinate system and the
representation of coordinates by using degrees, minutes and seconds. Try incorporating
Cartesian coordinate systems from mathematics class. Geographic coordinate systems are a way
of assigning a unique positional value to every location on Earth. DMS is one way of assigning
those values, where degrees are the angular measure from some central point, such as the
equator, to the location. Minutes and seconds are merely smaller divisions of degrees. Other
associated concepts may be also presented, including cardinal points (North, East, South and
West directions), parallels (lines of latitude) and meridians (lines of longitude).
17) Fill out TABLE 1 by selecting multiple sample points over the unsupervised classification image.
Use the first two columns of this table to list the sample coordinates (in latitude and longitude).
Enter also the land cover class resulting from your analysis of the unsupervised classification and
the Landsat-TM image. Then, use the coordinates of each sample to display the same location in
EarthExplorer. In the last column of TABLE 1, enter your interpretation of the land cover at each
sample location, based on your visual interpretation of the EarthExplorer image. While working
on this, make sure:
a. You select approximately the same number of points for each class (for instance, if your
classification has five classes, you can select five points to represent each of the classes,
totaling 25 samples).
b. You select points that are relatively well distributed over your classified image and not
concentrated in a single particular location. Classification might be great in one section
of the image, but poor in others.
Note to Student: The EarthExplorer image will probably show more detail than your Landsat-TM
image. Because of that, it is expected that EarthExplorer will have multiple ground elements
(represented by multiple pixels) inside a single Landsat-TM pixel. In most cases you can choose
the predominant land cover type around a given coordinate when evaluating the results of
image classification. For example, coordinates from the classified image may direct you to a gap
in a forest, where you may be able to see bare soil and shadow, surrounded by trees. If the gap
is not very large, the predominant land cover type contributing to the final Landsat-TM pixel
value may still be forest.
18) After you finish filling out TABLE 1, you can calculate the accuracy of your classification. To do
so, use TABLE 2 (an Error Matrix, see below) to compile the data from TABLE 1, considering the
following:
a. Each cell of TABLE 2 should have the number of times a class from your classification
corresponded to a certain class from your reference data (EarthExplorer image).
b. Notice that diagonal cells indicate a match between your classification and the
reference data.
c. Although space for seven classes is provided, you may not have all these classes around
your school and can leave cells blank.
d. After you have finished transferring the counts resulting from your classification
comparison into TABLE 2, calculate totals for rows and columns by:
i. Adding all cell values for the first column and enter this value at the
bottommost cell of that column. Repeat the same procedure for the other
columns, with the exception of the last column.
ii. Adding all cell values for the first row and enter the total in the rightmost
column. Repeat the same procedure for all rows.
e. To calculate the overall accuracy of your classification, add the values for the cells in the
diagonal, excluding the lower-right cell, and divide the total by the value in the lowerright cell of the table. If you multiply your result by 100, you will get an estimate of the
percentage of the points that were correctly classified in relation to the total number of
classified points.
Sample Error Matrix
Reference/Validation Data
Dark soil
Dark soil
Green
Vegetation
Non-green
vegetation
Constructed
2
1
Light soil
Unsupervised Classification
Light Soil
3
Green
vegetation
Non-green
vegetation
2
2
Constructed
1
1
2
4
3
3
Row totals
2
4
3
4
1
4
4
2
Water
Column
totals
Water
6
4
4
4
6
24
Sum of the diagonal = 14
Total points sampled = 24
Overall accuracy: 14/24 = 0.583 = 58.3%
f.
Calculate also the accuracy of the classification for each class, in relation to the total
number of samples for the class. This accuracy calculation is called User’s Accuracy. and
provides a quantitative measure of how many pixels are actually what they say they are.
To do that, use the Error Matrix and Fill in Table 4 with the numbers of correct
classifications (the diagonal numbers) and the number of total samples (the column
totals in the bottom row). To calculate the accuracy column, divide the diagonal value
for the each row by the row total, as shown below.
Note to Teacher: Although other ways to estimate classification accuracy exist, for simplicity, we
are restricting this tutorial to the calculation of the User’s Accuracy only. Other examples include
Producer’s Accuracy.
g. Do all your classes show the same User’s Accuracy values? Think about potential
reasons for the classification accuracy to be different when considering different classes.
Sample User’s Accuracy Calculation
Correct classification
Total samples
Accuracy
Dark soil
2
4
2/4 = 50%
Light soil
1
4
1/4 = 25%
Green vegetation
3
4
3/4 = 75%
Non-green vegetation
2
4
2/4 = 50%
Constructed
2
4
2/4 = 50%
Water
4
4
4/4 = 100%
Class
19) Be sure you have answered all the discussion questions at the end of this tutorial. Following
your teacher’s instructions, you may wish to print your results or discuss them with the class.
Congratulations!! You just finished the accuracy assessment part of the unsupervised classification
tutorial, using a Landsat-TM image and the MultiSpec© image analysis software.
Note to Teacher: The following pages contain student questions and the tables used in this tutorial. We
suggest printing these pages and providing them to your students as worksheets.
Student name/group: ____________________________________________ Date: _____/_____/______
TABLE 1 – Accuracy Assessment Tutorial: Classification results and reference data comparison for sample
points
#
Latitude
Longitude
Land Cover
(Unsupervised Classification)
Land Cover
(EarthExplorer image)
1
Example: 33.9517
-83.3876
Green vegetation
Constructed
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Student name/group: ____________________________________________ Date: _____/_____/______
TABLE 2 – Error matrix for unsupervised classification
Reference/Validation Data
Unsupervised Classification
Row
totals
Column totals
Diagonal total = ______
Overall accuracy = ________ = ________%
Total number of points sampled = ______
Student name/group: ____________________________________________ Date: _____/_____/______
User’s Accuracy Calculation
Class
Correct classification
Total samples
Accuracy