1. Class 10 Unsupervised Classification
Histogram Classification
Sequential Clustering
ISODATA Clustering
Grouping Based on Spectral Similarity

2. Definition
Unsupervised classification is a process
of grouping pixels that have similar spectral
values and labeling each group with a class
Supervised classification is to classify an
image using known spectral information for
each cover type
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3. 1. Histogram-based unsupervised classification
spruce
rocks
willow
water

4. Classification thresholds based on histogram peaks
Spectral Class
Range of Digital Values 1
Less than 60 2
61 through 125 3
126 through 162 4
Greater than 162

5. One-band image, example
230
201 77 73 68
143
147
153
102 89
139 23 15 98 91
137 26 18 94 90
125
222 13

6. One-band image, classified4 2 3 1

8. 2. Sequential Clustering (K-mean clustering)
By assigning pixels in some sequence to
different classes according to spectral
distance of each pixel from the mean of
each class

9. Spectral distance i. Two band spectral distance Spectral distance
=(x2-x1)2+(y2-y1)2
(x2,y2)
Band 2
(x1,y1)
Band 1

10. Spectral distance ii. Three band spectral distance Spectral distance
=(x2-x1)2+(y2-y1)2
+(z2-z1)2
iii. In general:
Spectral distance = [(xi1-xi2)2]0.5 n
i=1

11. Procedures for sequential clustering
Pass#1 (for a subset of the image using a skip factor)
Step1: define maximum allowable number of spectral classes Cmax
define maximum allowable spectral distance between classes Dmax
Step2: start at pixel (1,1) and proceed sequentially from left to right .
For a pixel, find D for all existing classes, i.e., D1, D2, D3,…Dn.
If all Di<Dmax, assign the pixel to the class which has the
smallest D.
If Di>Dmax, and C<Cmax create a new class
Pass#2 (for the whole image)
Start at pixel (1,1), process pixel by pixel. A pixel will be assigned to a
class which has the minimum spectral distance from the pixel.

12. Example (two band image to classify)50 43 78 65 88
123
125 99
244
233 59 49
128 98
209
154
117 67 33
193
198
231
205
132 22
141
245
241
109 75
239
202
100 38 58
249 14
189
217
156
114 48

13. Sequential Clustering Criteria Example
Maximum allowable number of spectral classes =10
Maximum allowable distance to spectral class mean=40

17. Example (two band image classified by SC)1 2 3 4 5 6 7 8 9 10
Next task: to label the classes

18. 3. ISODATA clustering (Interactive Self-Organizing Data Analysis)
It is an unsupervised classification method
opposite to the sequential clustering.
It starts with all pixels as a class and gradually
splits it to a desired number of classes
Verbyla Chapter 6

19. Criteria of ISODATA
Starting criterion: initial number of spectral classes
Processing criteria:
Splitting criterion: maximum variation of spectral class
Merging criteria: maximum distance between classes
maximum number of classes
maximum number of members in a class
Iii. Stopping criteria: maximum iterations
desired percent unchanged classes

20. Procedures of ISODATA Pass #1 Pass #2
Split any class that is too variable, i.e., the standard deviation
exceeds the maximum allowed.
Merge any spectral classes that are too close, i.e., distance
between class means is less than the minimum specified
Pass #2
Classify pixel by pixel according to the minimum distance from
the class centers found in pass #1

21. Example Criteria: Initial number of classes =1
Maximum standard deviation = 40
Maximum distance between classes =5
Maximum classes =5
Minimum number of members (pixels) in a class =2
Maximum number of iteration =3
Desired percent unchanged class members=90%

26. Example (two band image classified by ISODATA)1 2 5 4 3
Next task: to label the classes

27. 4. Grouping based on Spectral Similarity
This provides the basis for further reducing the number of classes
obtained using other unsupervised classification methods.
Mean digital values
Spectral Classes
Band 1
Band 2 1 10 5 2 20 3 30 55 4 40 50 90
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28. 4. Grouping based on Spectral Similarity
This provides the basis for further reducing the number of classes
obtained using other unsupervised classification methods.
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