Digital Image Classification
 Uses the spectral information represented by
the digital numbers in one or more spectral
bands.
pixel based on this Classifies each individual pspectral information. Also known asspectral pattern recognition.
 The objective: To assign all pixels in the image toparticular classes or themes (e.g., water, coniferous
forest deciduous forest
forest,forest, crops
crops, bare soil
soil, etcetc.))David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

1 / 72

Digital Image Classification

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

2 / 72

Digital Image Classification
Digital ImageClassification

Residential
Commercial
Transportation
Green crops / grass
Forests
Bare mineral soils
Bare organic soils
Water

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

3 / 72

Digital Image Classification
 The two approaches: Per-pixel classification• The algorithm categorizes each input pixel intoa spectral feature class based solely on its
individual multispectral vector (signature).
(signature) No
context or neighborhood evaluation is involved.

 Object-oriented classification• The input pixels are grouped into spectralfeatures (objects) using an image segmentation
algorithm. These objects are characterized inboth the raster and vector domains. The objects
are classifiedl
ifi d usingi b
bothth spectralt l andd spatial
ti lcues (metrics).
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

4 / 72

Digital Image Classification
 The three common per-pixel methods are:
 Supervised classification
 Unsupervised classification
 Rule-based classification

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

5 / 72

Digital Image Classification
 Supervised classification
 The analyst "supervises” the categorization of a setof specific classes by providing training statistics
that identify each category.

 Unsupervised classification
 The raw spectral data are grouped first, based
solelyy on the statistical structure of the data. Thenthe analyst must label each statistical cluster,
placing them into the appropriate categories (ifpossible).
possible) Rule-based classification
 Spectrally categorized pixels are classified using
ancillary data in a GIS model.
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

6 / 72

Digital Image Classification
 Supervised classification
 In the imagery, the analyst identifies homogeneous,representative examples of the various surface
cover types (information classes) of interest.
 These samples are referred to as training areas.
 The selection of appropriate training areas is basedon the analyst's familiarity with the geographicalarea and their knowledge of the actual surface
co er ttypes
cover
pes present in the image
image.
 Good visual interpretation skills are mandatory
f success.
forDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

7 / 72

Digital Image Classification
 Supervised classification
Spectral
Signatures

Trainingg Sites

Conifers

Water
Deciduous

The computer sooftware com
mpilesConifer

Water

Used to classifyy
all unknown
pixelsD d
Deciduous

Digital ImageDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 20158 / 72

Digital Image Classification
 Supervised classifiers (algorithms)
 Parallelpiped – based on range or variance of class DNs
 Minimum Distance-to-Means – based on mean class DNs
 Maximum Likelihood – based on probability of classmembership Spectral Angle Mapper – class membership based onminimum difference from the n-dimensional spectral
vectors of the classes

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

9 / 72

Digital Image Classification
 Supervised classification process Step 1: Define training data Each training site should appear homogenous andrepresentative of the legend class Delineate several training sites for each legend class Make each training site at least 20-25 pixels (i.e., > 5 acresfor 30 meter pixels)
 Each class should be represented by ~ 100 n pixels (where n= number of spectral bands in the data set)

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201510 / 72

Digital Image Classification
 Supervised classification process Trainingg Sites
 For 6-band TM & ETM imagery, the total number oftraining pixels per class should be at least 600
 Try to capture the landscape diversity of the class600
pixels

100

100
100

100
100
100

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201511 / 72

Digital Image Classification
 Example of a homogeneous training site

Check all bands

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201512 / 72

Digital Image Classification
 Example of a heterogeneous training siteDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201513 / 72

Digital Image Classification
 Supervised classification process Trainingg data Should be normally distributed if the maximum
likelihood classifier is going to be used

Darker DNs
David P. Lusch, Ph.D.

Brighter DNs
GEO 827 – Digital Image Processing and Analysis

October 201514 / 72

Digital Image Classification
 Supervised Classification Process
Collect
ect seveseverala trainingt a g sites
s tes foro eaceach classc ass in ordero e to Co
capture the landscape diversity of the class
Deep, clear water

Turbid water

A dark versionof class 1

Darker DNs

A bright versionof class 1- 1 +1



- 1

+1



Brighter DNs

The advantage of individual signatures:
separate mean DN values and small variancesDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201515 / 72

Digital Image Classification
 Supervised Classification Process For variable classes do NOT merge
g signaturesgDarker DNs

- 1

+1

Brighter DNs


The disadvantage of merged signatures:shifted mean DN value (atypical of the class) and a large varianceDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201516 / 72

Supervised Classification
 Training Sites – Tools in ERDAS Imagine
 Drawing tab: Insert Geometry Tools
 Polygon tool
 Region Grow tool

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201517 / 72

Supervised Classification
Polygontool

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201518 / 72

Supervised Classification
 Drawing Tools  Region Grow tool
Step 1: Set Seed Properties:

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201519 / 72

Supervised Classification
 Drawing Tools  Region Grow tool
Step 2: Select a seed
pixel with the Region
Grow Tool:

Note excluded areas

Selected seed pixel

GrownregionDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

20 / 72

Supervised Classification
 Drawing Tools  Region Grow tool
Step 3: Evaluate Spectral Euclidean Distance:

Spectral Euclidean Distance = 15 Spectral Euclidean Distance = 5using
g 8‐bit imagery
g yWith 16‐bit imagery, SED ~ 900 to 2000 are appropriate
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

21 / 72

Supervised Classification
 Training Sites – Tools in ERDAS Imagine Raster tab Classification Group Supervised Signature Editor

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201522 / 72

Supervised Classification
 Training Sites – Tools in ERDAS Imagine
 Signature Editor Create New Signature(s) from AOI(s)David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201523 / 72

Supervised Classification
 Training Sites – Tools in ERDAS Imagine
 Signature Editor Evaluate each new signature Rename the class tosomething meaningful
 Change color to Yellow for Image
Alarm evaluation

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201524 / 72

Supervised Classification
 Training Sites – Tools in ERDAS Imagine
 Signature Editor View  Image Alarm

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201525 / 72

Supervised Classification
 Signature Editor View  Image Alarm
1.
2.

3.
2 SD eliminates the darkest and brightest 2.3% of the data
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

26 / 72

Supervised Classification
 Signature Editor View  Image Alarm Set Std. Deviation between:
 2.00 (95.4% of the data)
 3.00 (99.7% of the data)• 1.00 SD = 68.3% of the data

Rationale:at o a e Eliminate
ate thet e most
ost atypicalatyp ca pixel
p e values
a ues fromo coconsideration.
s de at o
Image Alarm uses the simple Parallelepiped classifier in which
each class is defined as an n-dimensional volume whosebboundaries
d i iin b
brightness
i ht
space are sett b
by ththese lilimits.
itDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201527 / 72

Supervised Classification
 Visually evaluate Image Alarm for:
 Errors of Omission
 Errors of Commission

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201528 / 72

Supervised Classification
 Errors of Omission
 Indicates the need for and location of additional training
sites to adequately capture the spectral variability of theclass in question.

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201529 / 72

Supervised Classification
 Errors of Commission
 Indicates that the training site is not spectrally “pure”
regarding the assumed land cover/use classclass.
 The analyst must go back to “constrict” the training site
(spectrally) by editing the existing training polygon or
drawing a new one.
 General rule: “Do no harm” ((i.e.,, it is better to leave somepixels unclassified (omission errors) rather than to havelarge commission errors).

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201530 / 72

Digital Image Classification
 Maximum Likelihood Probability classifier
 Without A Priori Probability Information
 Decide unknown measurement vector X is in class i if, andonly if, pi > pj for all i and j out of 1... m possible classes
and

1
1

T
1
pi  log e | Vi |    X  M i  Vi  X  M i 22Where | Vi | is the determinant of the covariance matrix ,  X  M i T is thetransposepof the vector  X  M i  and Vi 1 is the inverse of the covariance matrix .
M i is the mean measurement vector for class i, and Vi is the covariance matrixof class i for bands k through l. Therefore, to assign the measurement vector Xof an unknown pixel to a class, the maximum likelihood decision rule computes
th valuethel pi forf eachh classl andd assigns
itheth pixeli l tot theth classl thatth t hash theth largestlt(or maximum) value.
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201531 / 72

Digital Image Classification
 Maximum Likelihood Probability classifier
 Without A Priori Probability Information
 The mean vectors M i and covariance matrix Vi for eachclass are estimated from the training data.

1
1

T
1
pi  log e | Vi |    X  M i  Vi  X  M i 22Where | Vi | is the determinant of the covariance matrix ,  X  M i T is thetranspose of the vector  X  M i  and Vi 1 is the inverse of the covariance matrix .
M i is the mean measurement vector for class i, and Vi is the covariance matrixof class i for bands k through l. Therefore, to assign the measurement vector Xof an unknown pixel to a class, the maximum likelihood decision rule computes
the value pi for each class and assigns the pixel to the class that has the largest(or maximum) value.
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201532 / 72

Digital Image Classification
 Maximum Likelihood Probability classifier
Unknownpixels:•1•2•3

70% probability
80% probability
90% probability

Unknown p
pixel 1is assigned toclass H because
p1H
1 > p1R
1 >> p1J
1

Red

Unknown pixel 2
is assigned toclass R because
p2R > p2J >> p2Hn-dimensionalclass means

Unknown pixel 3
is assigned toclass R because
p3R > p3H >> p3J

NIRDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201533 / 72

Digital Image Classification
 Maximum Likelihood Probability classifier
90
90

80

80

70

70

60

60

50

40
50
30
40
20
30
10
20

10

Adapted from Jensen, 2005

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201534 / 72

Digital Image Classification
 Maximum Likelihood Probability classifier Advantages
 Sensitive to both variance and covariance in the training data Disadvantages
 Training data must be normally distributed
 The per-pixel, rather than pixel neighborhood approach,remains a problem

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201535 / 72

Digital Image Classification
 Maximum Likelihood Probability classifier Multi-band Variance,
Variance Covariance and Correlation

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201536 / 72

Digital Image Classification
 Maximum Likelihood Probability classifier Variance – Covariance Matrix
211 = 5192.16

212 = 3866.65

213 = 2722.83

214 = 1094.98

222 = 3781.67

223 = 2520.50

224 = 1462.00

233 = 4806.52

234 = 3652.66
244 = 3927.78

Sum of variances = 17708.13

4-band data set (range 0 – 255)
Mean band 1 = 129.29
129 29Mean band 2 = 106.63
Mean band 3 = 104.71
Mean band 4 = 121.80

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201537 / 72

Supervised Classification
 Maximum Likelihood Classifier Raster tab Classification Group Supervised
1
2

3

4

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201538 / 72

Supervised Classification
 Maximum Likelihood Classifier All pixels are classified initially, irrespective of how low the
probabilities of class membership areare.
 E.g., assume the probabilities of pixel i belonging to oneof eight classes is: p1 = 0.0125
 p2 = 0.0237
 p3 = 0.0105
Pixel i is assigned to Class 5 even though its
 p4 = 0.0095 probability of class membership is only
2 75% (i(i.e.,e it
it’ss 97.25%
97 25% likely to NOT be a
 p5 = 0.0275
0 0275 2.75%
member of Class 5 (and even less likely to be p6 = 0.0078 a member of any other class).
 p7 = 0.0083
 p8 = 0.0195
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201539 / 72

Supervised Classification
 Maximum Likelihood Classifier Threshold tool (Raster tab Supervised Threshold) Thresholding is a statistical method used to refine a supervisedclassification by determining which pixels in the new thematic
raster layer are most likely to be incorrectly classified (using
the distance file that was created during the supervisedclassification). These distances occur in probability space, solarger distances are associated with lower probabilities of classpmembership.
 The histogram of the distance file usually approximates a chisquare distribution. Normally, there are many pixels with adistance image value at or near 0, indicating that these pixels
are likely to be classified correctly. A histogram is generated
for each class in the classified image file.
 Analyst displays
displays, inspects and edits (thresholds) each histogram.
histogram
The rejected pixels become “unclassified.”David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

40 / 72

Supervised Classification
 Maximum Likelihood Classifier Threshold tool (Raster tab Supervised Threshold)
Keep

RejectDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

41 / 72

Digital Image Classification
 Spectral Angle Mapper n-dimensional vectors

Hyperangle  < hyperangle ,
S theSoth Unknown
U k
pixel
i l iisclassified as a member ofclass “A”

255

Unknown (165,155,127)
Known A

Band B


155



255
Known B
127

0

165

255

Band ADavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201542 / 72

Digital Image Classification
 Spectral Angle Mapper
nb




tr


1 
i
  cos 

1
/
2
1
/
2
nb
nb
  t 2   r 2  

i

 i
 
 i   i
Where nb is the number of bands in the image, t is the unknown
pixel spectrum, and r is reference spectrum.

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

43 / 72

Digital Image Classification
 Spectral Angle Mapper Given a set of reference signaturesgcollected froman image with m bands, the SAM classifier performs
the following two steps:– Step 1: Determine the spectral angle  i,r,
between a pixel i in the image and every
referencefclass
l r: nb
1 
 i ,r  cos David P. Lusch, Ph.D.



m

nb
nb
2
2 
t
r
m i,m m r ,m  ti,m rr ,mGEO 827 – Digital Image Processing and Analysis

October 201544 / 72

Digital Image Classification
 Spectral Angle Mapper– Step 2: Assign each pixel to the reference class rthat has the smallest spectral angular distance
between pixel i and reference class r. For eachpixel i = 1 to n, find the reference class r such thatthe  i,r, is the minimum of all reference classes.

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201545 / 72

Digital Image Classification
 Spectral Angle Mapper Classifier Advantagesg Often more accurate than maximum likelihood
 Less sensitive to illumination differences

strongly
illuminatedBand A Disadvantages
 Numerous training spectra
required to describe the“best” mean vector foreach classmoderately
illuminated

dimly
illuminatedBand BDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201546 / 72

Supervised Classification
 Spectral Angle Mapper Raster tab Classification Group Supervised
1
2

3

4

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201547 / 72

Supervised Classification
 Image Classification
 Classifications can always be improved - perfection israrelyl possible,
ibl especially
i ll using
i precisei LULC categoriesti Per pixel classification works best with moderate spatial
resolution
l ti iimagery (GSD >= 20 meters)t )The classification process is iterative – it’s rare to havehigh classificationfaccuracy after
f the firstfattempt
 Consider a hybrid classification – part supervised, part
unsupervised (especially in areas where your field
experience is limited) Manual Classification and/or Editing is not cheating, but
can be extremely time consumingDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

48 / 72

Digital Image Classification
 Supervised classification
 The analyst "supervises” the categorization of a setof specific classes by providing training statistics
that identify each category.

 Unsupervised classification
 The raw spectral data are grouped first, based
solelyy on the statistical structure of the data. Thenthe analyst must label each statistical cluster,
placing them in the appropriate categories (ifpossible).
possible) Rule-based classification
 Spectrally categorized pixels are classified using
ancillary data in a model.
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201549 / 72

Unsupervised Classification
 Requires only minimal initial input from theanalyst.
 However, the analyst has the task of interpretingthe many classes that are generated by theunsupervised classification algorithm.
 Unsupervised
Uns per ised classification is also called
clustering, because it is based on the naturalgroupings of pixels in image data when they areplotted in feature space.

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

50 / 72

Unsupervised Classification
 Clusters are defined with a clustering algorithm.
g algorithmghas no regardgfor the The clusteringcontiguity of the pixels that define each cluster
unlike the training areas for supervisedclassification.
lifi i
 The Iterative Self-Organizing Data Analysis –ISODATA clustering method uses spectral
distance and iteratively classifies the pixels.
 After each iteration, ISODATA redefines the criteria foreach class, and classifies again, gradually “discovering”
the spectral distance patterns (i.e., the clusters) in thedata.
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

51 / 72

Unsupervised Classification
 The initial cluster means are evenly distributed
across the feature space alongg a vector thatruns from the multidimensional -1  (1-1, 2-2, 3-3, ... n-n)

to the multidimensional +1  (1+1, 2+2, 3+3, ... n+n)David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201552 / 72

Unsupervised Classification
 ISODATA Initialization

User specifiedU
ifi dnumber ofclusters

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

53 / 72

Unsupervised Classification
 ISODATA uses minimum spectral distance toassign a cluster for each candidate pixel.
 The process begins with a user-specified number ofarbitrary cluster means (or the means of existing
signatures).
signatures) With each iteration, the means shift to those of the clusters
in the datadata.
 Because ISODATA is iterative, it is not biased to the top of
the data file
file, as are the one-pass
one pass clustering algorithms
algorithms.
 The spectral distance between any candidate pixel andeach cluster mean is calculated and the pixel is assigned
to the cluster whose mean is the closest.
David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

54 / 72

Unsupervised Classification
 Multispectral Feature SpaceDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

55 / 72

Unsupervised Classification
ISODATA first pass

Initial arbitrary
multi-band means

Initial cluster
boundaries

Adapted from Jensen,
Jensen 2005

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

56 / 72

Unsupervised Classification
ISODATA second pass

New multi-bandcluster means

New cluster
boundaries

Adapted from Jensen, 2005

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201557 / 72

Unsupervised Classification
ISODATA nth pass

Final multi-bandcluster means

Final cluster
boundaries

Adapted from Jensen, 2005

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

58 / 72

Unsupervised Classification

Adapted from Jensen, 2005

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201559 / 72

Unsupervised Classification
 ISODATA in ERDAS Imagine Raster tab Classification Group Unsupervised

Use only for hybrid classification:
1) ISODATA of small, representative area
2) Cluster interpretation by analyst
3) Cluster Signature Set used in Maximum
Likelihood classifier (with multiple
same-class sigs colored the same)

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201560 / 72

Unsupervised Classification
 ISODATA in ERDAS Imagine
Step 1
Step 5
Step 2

As a generall rule,A
l thi
this should
h ld bbe att
least 3x the number of legend
classes you desire + 3 – 5 more

Step 3
Step 4

Step 6

Minimum Size = 1 – 5%
Maximum SD = 3.0
3 0 – 4.040

Minimum Distance = 4 – 5
Step 7(for 8-bit images)
Maximum Merges = 10 - 15

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

61 / 72

Unsupervised Classification
 ISODATA in ERDAS Imagine
Step 8

Step 9

Step 10

Typically requires 15 – 20 iterations
to stabilize (35 – 40 for 16-bit images)

Step 11

Set to 0.95 (0.96– 0.98 will take many
more iterations)

Step 12David P. Lusch, Ph.D.

Set to X:1,,Y:1(i.e., do not skip any pixels)

GEO 827 – Digital Image Processing and Analysis

October 201562 / 72

Unsupervised Classification
 After each iteration, the normalized percentage of
pixels whose assignmentspg
are unchanged
g sincethe last iteration is determined.
 When this number reaches T (the convergencethreshold), the program terminates.
 As a QA
Q check, ensure that the program
terminated because it reached the convergencethreshold and not because it reached the
maximum number of iterations.

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

63 / 72

Unsupervised Classification
 File Session View Session Log

David P. Lusch, Ph.D.

Performing
g iteration: 1

Convergence:g
0.000

Performing iteration: 2

Convergence: 0.260

Performing
g iteration: 3

Convergence:g
0.525

Performing iteration: 4

Convergence: 0.862

Performing
g iteration: 5

Convergence:g
0.920

Performing iteration: 6

Convergence: 0.938

Performing
g iteration: 7

Convergence:g
0.949

Performing iteration: 8

Convergence: 0.957

GEO 827 – Digital Image Processing and Analysis

October 201564 / 72

Unsupervised Classification
20 Clusters
6 Iterations

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201565 / 72

Unsupervised Classification
 Cluster Labeling Attribute Table
Right-click for
pop-up menu.
Select “Display
Attribute Table
Table”

Attribute
Table

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

66 / 72

Unsupervised Classification
 Cluster Labeling Attribute Table
1. Left-click on Opacity column heading to select all
2. Right-click on Opacity column heading toopen “Column Options” pop-up menu
3. Select “Formula” from menu

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201567 / 72

Unsupervised Classification
 Cluster Labeling Attribute Table

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

68 / 72

Unsupervised Classification
 Cluster Labeling Attribute Table
Inspect theincluded LULC
classes in eachclusterl t usingi ththeSWIPE tool
(HOME tab)David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

69 / 72

Unsupervised Classification
 Cluster Busting It is common when performing unsupervised classification thatthe algorithm will generate n clusters, but you have no confidence
in labeling q of them to an appropriate information class (e.g., 15
out of 30 clusters contain multiple LULC categories).
 Step 1 - all the pixels associated with the 15 clusters that are mixed (e.g.,clusters 3, 5, 6, 9, 11, 13, 15, 17, 18, 20, 22, 23, 25, 28 and 30)) are all RECODED
to a value of 1 and a binary mask file is created.
 Step 2 - A mask program is run multiplying the binary mask file times theoriginal imagery file.
file The output of the mask program is a new multiband
image file consisting of only the pixels that could not be adequately
labeled during the initial unsupervised classification.

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 201570 / 72

Unsupervised Classification
 Cluster Busting Step 3 - Perform a new unsupervised classification on the output file
f
from
Step
St 22, perhapshrequestingti th
the original
i i l numberb off clusters.
l t Step 4 – Repeat Steps 1 and 2, keeping all of new clusters that are
dominated byy a single
g LCLU (e.g.,( g , 7).
) There are likelyy still some mixed-classclusters, but the proportion of these is definitely smaller.
 Step 5 - Repeat Step 3 performing a third iterative unsupervisedclassification.
l ifi i P
Perhapshan additional
ddi i l 3 goodd clusterslare extracted.
dDavid P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

71 / 72

Unsupervised Classification
 Cluster Busting In this hypothetical example, the final cluster map would becomposed of :
 15 good clusters from the initial classification,
 7 good clusters from the first cluster
cluster-bustingbusting pass (recoded as values 31
to 38), and 3 good clusters from the second cluster-busting pass (recoded as values
39 to 41).

 The final cluster map file is re-assembled using a simple GIS
maximum-dominate
maximum
dominate overlay function.
function The final cluster map is thenrecoded to create the final classification map.

David P. Lusch, Ph.D.

GEO 827 – Digital Image Processing and Analysis

October 2015

72 / 72