Extracted for your convenience from The Remote Sensing Tutorial Goddard Space Flight Center,
NASA. Nicholas M. Short, Sr , Editing: Jon Robinson
http://teachserv.earth.ox.ac.uk/nasa/
Unsupervised Classification
In an unsupervised classification, the objective is to group multiband spectral response patterns into
clusters that are statistically separable. Thus, a small range of digital numbers (DNs) for, say 3 bands,
can fix one cluster that is set apart from a specified range combination for another cluster (and so
forth). Separation will depend on the parameters chosen to differentiate. This can be visualized with
the aid of this diagram, taken from Sabins, Remote Sensing: Principles and Interpretation. 2nd Ed. for
four classes: A = Agriculture; d= Desert; M = Mountains; W = Water.
From F.F. Sabins, Jr., Remote Sensing: Principles and Interpretation. 2nd Ed., © 1987. Reproduced by
permission of W.H. Freeman & Co., New York City.
These can be modified so that the total number of clusters can vary arbitrarily. When the separations
are carried out on the computer, each pixel in an image will be assigned to one of the clusters as being
most similar to it in DN combination values. Generally, in an area within an image, multiple pixels in
the same cluster will correspond to some (initially unknown) ground feature or class so that patterns of
gray levels will result in a new image depicting the spatial distribution of the clusters. These levels can
then be assigned colors to produce a cluster "map". The trick then becomes one of trying to relate the
different clusters to meaningful ground categories. This must be done by either being adequately
familiar with the major classes expected in the scene under study or, where feasible, by visiting the
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scene itself - ground truthing - and visually correlating map patterns to their ground counterparts.
Since the classes are not selected beforehand, this method is said to be unsupervised.
The Idrisi image processing program employs a simplified approach to unsupervised classification.
Input data consist of the DN values of the registered pixels for the 3 bands used to make any of the
color composites. Algorithms calculate the cluster values from these bands. The maximum number of
clusters is automatically determined by the parameters selected in the processing. This typically has
the effect of producing so many clusters that the resulting classified image becomes too cluttered and
thus more difficult to interpret in terms of assigned classes. To improve the interpretability, the
number of classes has been limited to 15 (reduction from an initial 28).
The first unsupervised classification operates on the color composite made from bands 2, 3, and 4.
Examine the resulting image
and try to make some sense of the color patterns as indicators of the ground classes you have learned
about in the above paragraphs. A likely conclusion that you will reach: some of the patterns do well in
singling out some of the features in some parts of the Morro Bay subscene. But, many individual areas
represented by clusters do not appear to correlate that well with what you thought was there.
Unfortunately, what is happening is a rather artificial subdivision of spectral responses from small
segments of the surface, in some instances simply the effect of slight variations in surface orientation
that changes the reflectances or perhaps the influence of what was termed "mixed pixels" in the
Overview. When we try another composite,
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bands 4, 7, and 1, the new resulting classification has most of the same problems as encountered with
the first composite. We are forced to conclude that unsupervised classification is too much of a
generalization and that the clusters only roughly match some of the actual classes. Its value is mainly
as a guide to the spectral content of a scene to aid in making a preliminary interpretation prior to
conducting the much more powerful supervised classification procedures.



TM Band 6 = red
TM Band 7 = green
TM Band 5 = blue
This convolves the thermal band, sensitive to emitted radiation, (with its lower resolution 120 m.
pixels) with two infrared bands, 5 and 7 (with their 30 m. pixels). In this color composite generated
with Idrisi, the thermal band image (check TM 6 again) exerts a dominating control. In the Idrisi
mapping two colors - reds and blues - greatly outweigh any greens contributed by light tones in band
7. The reds largely represent the warmer surfaces, and the blues the cooler, as recorded in band 6. Note
that the blues extend over a broader fraction of the slopes in the hills than might be expected from the
shadow effects seen in other bands; in other words, the bulk of the back slope areas receiving less
direct sunlight respond by returning less thermal radiation. However, some areas of blue that lie on the
fore slopes are occupied by the uplands vegetation at g and the grasslands at v which offset the direct
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heating effect through cooling by evapotranspiration. Other areas supporting vegetation, as at y, and
also along streams have dark red tones, not because they are hotter but their tonal expressions in 5 and
7 are darker (almost no blue or green inputs).
Both urban communities are characterized by several shades of red, with the street patterns showing
through owing to their sharper definition in bands 5 and 7. This is in part due to the "urban island"
thermal effect, the tendency of populated areas to be made warmer because of heat-absorbing
materials (darker streets, tar-covered roofs, etc.), reduction in surface areas maintaining vegetation,
and heat emitted from furnaces, air- conditioners, and other human activities. Fully perceptible is the
thermal plume emanating from the power plant (t) but the ocean sediments introduce no noticeable
effect.
Some areas seen as green in the composite include the beach bar and several of the extraction pits (u).
These surfaces highly reflect in most bands, hence, their light tones in 5 and 7 combine the blues and
greens assigned to these bands whereas their darkness in 6 (reflective materials do not heat up as
much) leaves red out.
Lets now experiment briefly with combining one of the longer reflective IR bands (7, assigned to
green) with the vegetation band TM 4 (blue) and the ocean water/sediment band TM 1 (expressed in
red) as produced by the Idrisi Composite function which takes three 8 bit bands and converts it to a
single 8 bit image that mimics the result of using the three bands to make a true 24 bit color image.
The result is a colorful rendition that, in some respects, shows certain features to their best advantage.



TM Band 1 = red
TM Band 7 = green
TM Band 4 = blue
Again, blues and reds are the prevailing colors, with greens subordinate. The blues almost totally
correlate with vegetation which is made to stand out in sharp contrast to most areas that either lack this
cover or now support dormant grasses, etc. (note the active grasslands in lighter blue). The reds are
tied into three principal surface classes: the waves (note the bluish-purple tones within them,
representing the band 4 contribution); the sediments; and the towns, with the red streets speckled by
blues from local vegetation. The greens are confined mainly to some specific features that are
relatively brighter in band 7 and darker in 1 and 4, including areas in the hills around Los Osos and
some uncultivated areas (m) in valleys. Note scattered greens along the otherwise bright slopes.
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Supervised Classification
Supervised classification is much more effectual in terms of accuracy in mapping substantial classes
whose validity depends largely on the cognition and skills of the image specialist. The strategy is
simple: conventional classes (real and familiar) or meaningful (but somewhat artificial) classes are
recognized in the scene from prior knowledge such as personal experience with the region in question,
or by identification using thematic maps or actual on-site visits. This allows one to choose and set up
discrete classes (thus supervising selection) to which identifying category names are then assigned.
Training sites, areas representing each known land cover category that appear fairly homogeneous on
the image (as determined by similarity in tone or color within shapes delineating the category), are
located and circumscribed by polygonal boundaries drawn (using the computer mouse) on the image
display. For each class thus outlined, mean values and variances of the DNs for each band used to
classify are calculated from all pixels enclosed in the site(s) (more than one polygon can be established
for any class). When DNs are plotted as functions of the band sequence (increasing with wavelength),
the result is a spectral signature or spectral response curve for that class (in reality for the assemblage
of materials within the site that interact with the incoming radiation). Classification now proceeds by
statistical processing in which every pixel is compared with the various signatures and assigned to the
class whose signature comes closest (a few in a scene do not match and remain unclassified; these may
belong to a class not recognized or defined).
Many of the classes to be constituted for the Morro Bay scene are almost self-evident - ocean water,
waves, beach, marsh, shadows. In practice, we could further sequester several such classes, as for
example, distinguishing between ocean and bay waters, but their gross similarities in spectral
properties would probably make separation difficult. Other classes that are likely variants of one
another - such as slopes that either face the morning sun as Landsat flew over versus slopes facing
away - might be warranted. Some classes are broad-based, being representative of two or more related
surface materials that might be separable at high resolution but are inexactly expressed in the TM
image: in this category we can include trees, forest, and heavily vegetated areas (golf course; farm
fields).
For the first attempt at a supervised classification, 13 discretional classes have been formalized. The
outlines of their training sites are traced on the true color (bands 1,2,3) composite, as shown (their site
colors are assigned here for display convenience and do not correspond to their class equivalent colors
in the maps shown on the next page).
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Note that Idrisi does not actually name them (they are numbered and given names [tied to the
numbers]) during the stage when signatures are made. Several classes gain their data from more than
one training site. Idrisi has a module, SIGCOMP, that plots the signature of each class. Here we show
plots for clear seawater (light blue) and water with three different sediment densities (green, brown,
blue-green) and surf waves (yellow-green).
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It also has a program that presents pixel information for each signature, recording the number of pixels
contributing to the data, and the mean, maximum, minimum, and standard deviation of DN values for
each signature. To help you get a deeper feel for the numerical inputs involved in these calculations,
we have reproduced a simplified version of these data in the following table:
Table of
Band
Means and
Sample
Size for
Each Class
Training
Set
BAND:
Class
1. Seawater
2. Sediments1
3. Sediments2
4. Bay
Sediment
5. Marsh
6. Waves
Surf
7. Sand
8. Urban1
9. Urban2
10. Sun Slope
11. Shade
Slope
12.
Scrublands
13. Grass
14. Fields
15. Trees
16. Cleared
No. of
1
2
3
4
5
6 (TH)
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Pixels
57.4
62.2
69.8
16.0
19.6
25.3
12.0
13.5
18.8
5.6
5.6
6.3
3.4
3.5
3.5
112.0
112.2
112.2
1.5
1.6
1.5
2433
681
405
59.6
20.2
16.9
6.0
3.4
111.9
1.6
598
61.6
22.8
27.2
42.0
37.3
117.9
14.9
861
189.5
88.0
100.9
56.3
22.3
111.9
6.4
1001
90.6
77.9
68.0
75.9
41.8
32.3
27.0
31.7
54.2
39.3
32.7
40.8
43.9
37.5
36.3
43.5
86.3
53.9
52.9
107.2
121.3
123.5
125.7
126.5
52.8
29.6
27.7
51.4
812
747
2256
5476
51.8
15.6
13.8
15.6
14.0
109.8
5.6
976
66.0
24.8
29.0
27.5
58.4
114.3
29.4
1085
67.9
59.9
55.8
73.7
27.6
22.7
19.6
30.5
32.0
22.6
20.2
39.2
49.9
54.5
35.7
37.1
89.2
46.6
42.0
88.4
117.4
115.8
108.8
127.9
39.3
18.3
16.6
45.2
590
259
2048
309
You can deduce from the table on the previous page, that, dependent on the actual standard deviations
(not shown), most of the signatures have combinations of DN values that would appear to allow their
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distinctions from one another. Two classes - Urban 1 and Cleared [Ground] - are quite similar in the
first four bands but apparently are different enough in bands 5 and 7 to suppose that they are separable.
The range of variations in the thermal band 6 is much smaller than in other bands, suggesting its
limitation as an efficient separator. However, as will be seen below, its addition to the Maximum
Likelihood Classification increases the spatial homogeneity of some classifications
Minimum Distance Classification
We initiate our survey of supervised classification by producing one using the minimum distance
routine. The Idrisi program acts on DNs in multidimensional band space to organize the pixels into the
classes chosen. Each "unknown" pixel is then placed in the class closest to the mean vector in this
band space. For Morro Bay, the resulting classification image consists of 16 gray levels, each
representing a class, that can then be assigned on the computer any color one wishes; combinations are
usually selected to have either color themes (similar colors for related classes) and/or to set spatially
adjacent classes apart to the eye by using disparate colors. Examine this minimum distance
classification,
in which all 7 TM bands including the thermal participate. Study it in relation to your acquired
knowledge of this scene from the preceding pages in this section and compare it with the classification
we will now show.
Maximum Likelihood Classification
This next supervised classification is made using the maximum likelihood classifier. Again, multiband
classes are statistically derived and each unknown pixel is probabilistically analyzed to evaluate which
class it has the highest likelihood of belonging to. In the image next displayed thermal band 6 is
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omitted and 16 classes are defined (this is the maximum allowable in the Idrisi program). These are
identical to the previous ones recorded in the minimum distance image. In both instances, the sediment
has been subdivided into three levels (I and II in the ocean and a third in the Bay) and two urban
classes (I = Morro Bay; II = Los Osos) are attempted to account for visual differences between them
(mainly street patterns). Look at this image classification
and judge for yourself how believable is the result. Compare it with the minimum distance image as
well; to assist you in equating similar classes, the same color assignments are shared. Then, look at a
supervised classification
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using Band 6 and again specifying 16 classes; note how each urban area becomes more homogeneous.
Similar increase in spatial homogeneity of vegetation and generalized slopes is noted with band 6
added, but overall the differences between With and Without 6 are rather slight.
Your first impression is that each 16 class maximum likelihood version is a fairly dazzling image, with
many classes "right on". Both breakers and sand bar (beach) seem uniformly classified. The sediment
load distribution is credible. There are enough color tone differences between Morro Bay and Los
Osos to justify the decision to set them up as two classes (Los Osos differs in its street patterns and in
the presence of the orange-brown "soil" seen in the 1,2,3 composite) but color elements of one urban
class are mixed with the other, in differing proportions, as one would expect. The bright orange given
to the coastal marsh area occupies a slightly larger area than its equivalent does in the minimum
distance classification and is also distributed in small patches around the Los Osos coastline, and again
along the river (p) - probably a true condition in that such vegetation should be more widespread. No
doubt the most uncertain group of classes is spread over the hills. The categories Sun Lit Slope and
Shadow Slope are somewhat synthetic in that they refer mostly to an illumination condition whereas
the classes grass and trees may be a mix of lighting effects and actually a lighter or darker surface. The
class Cleared Land is again both a depiction of land surfaces that may support not only thin natural
vegetation or even be partially barren but also may in some places again be a shadowing effect. The
Grasslands is properly placed in this image but appears to spread over wider areas than indicated in
several other images - this is doubtless a valid case. The Green Vegetation category proxies well for
the actual distribution of reflective organic material (in band 4) but in this choice of class assignments
the several types of growing ground cover are not singled out. Thus, elements of the golf course and
the mountain crest forest are shown as "like" and are not distinguished from field crops, etc. They
could have been told apart to some degree of "correctness" if each had been given its own class and
training sites selected.
Nearly two years after the above supervised classifications were executed, an occasion arose to re-do
the same scene using new Idrisi software that operates from Windows rather than DOS 3.1. (that was
on Windows Version 1; Version 2 is now available). In performing this supervised classification, the
same Maximum Likelihood classifier was used with all 7 TM Bands and again 15 classes were set up.
But, as an experiment, it was decided to drop several class categories and select new ones instead.
Also, somewhat different training site polygons were established for each class. In effect, this achieved
an independent classification without "peeking" at the results shown above for guidance. And, instead
of using the natural color scene from which to pick training sites, the false color image was employed.
This is the result:
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Note that for nearly all classes different colors were assigned which makes it rather difficult to
compare the results with the earlier classifications. In the above image, one of the classes is omitted
from the Legend (a quirk of the image display in this Windows version); it is the class Trees, which is
rendered in Dark Green. Nevertheless, scrolling back and forth between this and the 7 band Supervised
Classification just above reveals both differences and similarities.
In the Windows version, the two sediment classes have been combined. Also, the class called Fields in
the 3.1 version is here renamed GreenVeg and includes not only fields in crop but also some natural
vegetation (probably local woodlands); both show as bright red in the false color rendition. The Trees
distribution is similar in both classifications but is a bit more widespread in the Windows version (but
harder to see because dark green and black shadows do not single out in good contrast). The classes
Scrubland and Cleared in the 3.1 version are partially represented by Scrub in the Windows version. In
3.1, Urban II (trained on the street pattern in Los Osos) is olive and is orange in the Windows version;
in both cases, the distribution of the Urban II class pattern is much more extensive than is the real
situation. Towns or clusters of buildings do not exist in the long orange strip near the highway nor in
the lower right part of the image. Apparently, some natural surfaces, as interpreted just from the true
and false color composite images, give rise to signatures that resemble this urban class. In the
Windows version, several very bright areas, mainly around Los Osos, have been named Sandpit. This
is a guess: they may be excavated ground or inland remnants of beach sand (although they classify as
distinct from the Sand Class); only an on-site visit could ascertain a correct identity. The point in
running and comparing these two classifications is probably obvious: the precise end result - mainly in
extrapolating classes from their training sites to the identities and distribution of the selected classes,
i.e., the overall appearance and accuracy of the classification - is sensitive to the variables involved
and the choices made. Interpretations will differ depending on the colors and other factors present in
the training image by which the classes are chosen as separable and efficient training sites blocked out.
The number of classes sought, the validity (purity) of the enclosed space representing these classes in
the training sites (and the number of pixels in the polygons assigned to each class), the nature of a
class (the urban division is somewhat artificial; scrub may in fact be composed of rather dissimilar
classes or features in the real world), the colors assigned to the final "map", and other considerations
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all contribute to differences. Once again, the argument is here emphasized that "field work", if
logistically possible, both before and after computer-based classification of an image is performed,, is
the key to selecting and then checking class locations and is thus the best insurance for achieving a
quality product. But, if an on-site visit is not feasible, a fairly reasonable classification can be
developed by a skilled interpreter based mainly on his/her abilities in recognizing obvious ground
features in the scene. The writer has achieved "believable" classifications of many parts of the world
without ever having been there just from his knowledge of the appearance of the common components
of a landscape or land use categories.
Probabilistic Neural Network Classifier
The Applied Information Sciences Branch (Code 935) at NASA Goddard Space Flight Center has
developed a program called Photo Interpretation Toolkit (PIT) which performs classification
operations similar to those we've introduced from Idrisi. The chief difference is in the mode of
selecting training sites. Instead of circumscribing these sites with polygons, as in Idrisi, the PIT allows
the user to block out continuous clusters of sample squares whose individual sizes can vary in width
that are displayed directly in the screen image at the site positions chosen. One of several different
classifiers can be selected to match unknown pixels in the image data set to those for classes with
similar statistics derived from the training site blocks. An example using PIT's Probabilistic Neural
Network classifier is shown on your screen. (Note: the person doing this classification at Goddard was
a programmer with limited experience in actually identifying classes.)
In this version, only 10 classes were arbitrarily established (the upper limit at the time). The resulting
product shows similarities to the Idrisi Minimum Distance version (in which 13 classes are specified)
but is a simplification of the latter that fosters easier interpretation. However, several classes show
notable misclassifications: areas of blue assigned to "ocean" are found scattered inland (these are
probably associated with "shadow" which has similar low DN values); the reds related to "marsh"
(which should be confined to the river delta) also appear in widespread places, including the higher
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mountains; the red- brown color given to "urban" is likewise found in too many places that are
certainly not urban. The orange, identified as shad2, is definitely not "shadows" but corresponds to
areas in the scene that have bright tones in the color composite images; this was a bad choice of a
class.
(Note: As announced in the Whats New button text in the Overview, sometime in 1998 an improved
version of PIT will be added (in an Appendix) to the CD-ROM version of this Tutorial which will
allow the reader/user to perform interactively a number of the image processing programs described in
Section 1. Among the several data sets to be included will be the Morro Bay subscene, of which you
are now quite familiar.)
Enough! If you have reached this point by working through this entire Section and reasoned along
with us in examining and analyzing the various Morro Bay images, you have become well-schooled in
the basics of image interpretation. You are ready, as curiosity prompts you, to call up the images in the
next scene - a geological study of a prominent fold structure in Utah - and any of the images in other
scenes that have been placed on line. Or, if you feel adventuresome after this exposure to image
processing, you may want to try your hand at carrying out your own processing on Morro Bay, using
the PIT processor, after teaching its procedures to yourself using the "cookbook" in Appendix 1.
_________________________________________________________________________
For further background, information, and reading underlying Principles of Computer Processing, with
emphasis on Remote Sensing, consult:
Avery, T.E and G.L. Berlin, Fundamentals of Remote Sensing and Airphoto Interpretation, Ch. 15,
Digital Image Processing, 1992, Macmillan Publ. Co.
Condit, C.D. and P.S. Chavez, Jr., Basic Concepts of Computerized Digital Image Processing for
Geologists, 1979, U.S. Geol. Surv. Bull. 1462, Wash. D.C.
Jensen, J.R., Introductory Digital Image Processing, 2nd Ed., 1996, Prentice-Hall, Inc.
Lillesand, T.M. and R.W. Kiefer, Remote Sensing and Image Interpretation, Ch. 10, Digital Image
Processing, 1987, J. Wiley and Sons, Inc.
Moik, J.G., Digital Processing of Remotely Sensed Images, 1980, NASA Special Paper 432, U.S.
Govt. Printing Office.
Sabins, F.F., Remote Sensing: Principles and Interpretation, Ch. 7, Digital Image Processing, 1987,
W.H. Freeman & Co.
Swain, P.H. and S.M. Davis, Remote Sensing - The Quantitative Approach, 1978. McGraw-Hill Book
Co.
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