Image Classification:
Introduction
Lecture Notes 7
prepared by R. Lathrop 11/99
updated 3/04
Readings:
ERDAS Field Guide 5th Ed. CH. 6:217-234
Where in the World?
Learning objectives
• Remote sensing science concepts
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Rationale and theory behind image classification
Image spectral vs. information classes
Unsupervised (ISODATA) algorithm
Clusterbusting
• Math Concepts
– Calculating mage distance
• Skills
– Applying ISODATA algorithm
Image Classification
One of the major applications of remotely sensed
imagery is to provide information on the amount and
spatial distribution of various types of land use and land
cover
land cover - the biophysical material covering the land
surface
land use - the use to which land is put by humans
Move toward more automated procedures using digital
image processing to map land use-land cover
Computer-assisted classification of
remotely sensed images
• Automatically categorize all pixels in an
image into land cover classes or themes
Convert image data into information
• Normally uses multi-spectral data and
spectral pattern recognition techniques as
compared to spatial or temporal pattern
recognition to aid in identification
Objective: Image to Thematic Map
Remotely Sensed Image
Classification
• 1st step: identify classification scheme to be
applied
• Hierarchical approach of increasing specificity
Level I: most general
Level II: more specific
• Level of classification depends on the spatial,
spectral, temporal and radiometric resolution
of the image data
National Land Cover Dataset
Classification system: 21 classes
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Water
11 Open Water
12 Perennial Ice/Snow
Developed
21 Low Intensity Residential
22 High Intensity Residential
23 Commercial/Industrial/Transportation
Barren
31 Bare Rock/Sand/Clay
32 Quarries/Strip Mines/Gravel Pits
33 Transitional
Forested Upland
41 Deciduous Forest
42 Evergreen Forest
43 Mixed Forest
Shrubland
51 Shrubland
Non-Natural Woody
61 Orchards/Vineyards/Other
Herbaceous Upland Natural/Semi-natural Vegetation
71 Grasslands/Herbaceous
Herbaceous Planted/Cultivated
81 Pasture/Hay
82 Row Crops
83 Small Grains
84 Fallow
85 Urban/Recreational Grasses
Wetlands
91 Woody Wetlands
92 Emergent Herbaceous Wetlands
http://landcover.usgs.gov/prodescription.asp
Feature Space Image
• Visualization of 2 bands of image data
simultaneously through a 2 band scatterplot
- the graph of the data file values of one
band of data against the values of another
band
• Feature space - abstract space that is defined
by spectral units
N
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R
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Spectral Feature Space
Red Reflectance
Each dot represents a pixel;
the warmer the colors, the
higher the frequency of
pixels in that portion of the
feature space
Spectral Pattern Recognition
• Numerical process whereby elements of
multi-spectral image data sets are categorized
into a limited number of spectrally separable,
discrete classes:
• 1) show (train) the computer the multiple
spectral band data associated with land cover
type of interest
• 2) the computer decides, using some form of
classification decision rule, which land cover
type each pixel most looks like
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Spectral Feature Space
Grass
Trees
Classification can be
Impervious thought of as trying to
relate spectral classes
Surface &
Bare Soil
or locations in the
feature space with the
appropriate
information class
water
Red Reflectance
Spectral vs. Information Class
• Spectral class - group (cluster) of spectrally
"like" pixels
• Information class - land use/land cover class
of interest
• May take many spectral classes to describe
one information class. One spectral class
may represent more than 1 information
class.
Spectral vs. Information Classes:
May take many spectral classes to describe
one information class. One spectral class may
represent more than 1 information class.
Spectral Class
Information Class
Sunlit conifer
Upland Conifer
Hillside shadowed conifers
Upland Deciduous
Deciduous broadleaf
Lowland Deciduous
Spectral Classes: pixels of one land cover
type tend to cluster together
Veg 1
NIR
Veg3
reflect
ance
Soil 3
Veg 2
Soil 2
Water 2
Soil 1
Water 1
Red reflectance
Adapted from J.A. Richards, 1986
Spectral vs. Information Classes
Veg 1
Soil Information
class
NIR
Veg3
reflect
ance
Soil 3
Veg 2
Soil 2
Water 2
Soil 1
Water 1
Red reflectance
Adapted from J.A. Richards, 1986
Spectral & information classes do not
always have a 1-to-1 match
Veg 1
NIR
Veg3
reflect
ance
Veg 2
Water 2
Same spectral class may
belong to more than one
information class
Developed 3
Soil 3
Developed 2
Soil 2
Developed 1
Soil 1
Water 1
Red reflectance
Adapted from J.A. Richards, 1986
Classification Process
• 1) Training/Clustering Stage - the process of
defining criteria by which spectral patterns are
recognized, developing a numerical description
for each spectral class
• 2) Classification Stage - each pixel in the image
data set is categorized into the spectral class it
most closely resembles based on a mathematical
decision rule
• 3) Output Stage - results are presented in a
variety of forms (tables, graphics, etc.)
Multispectral classification
Multispectral image classification using spectral pattern
recognition often relies on measuring the “likelihood”
that a pixel belongs to one class vs. another. This
likelihood generally relies on some measure of distance
between a pixel and the various spectral classes clusters.
For example, if a pixel is “closest” to Spectral Class 1 vs.
Spectral Class2, then the pixel is classified into spectral
Class 1.
Spectral distance can be measured in several ways:
- as simple euclidean distance in multispectral space
- as a statistical distance or probability
Spectral distance
• Spectral distance - the Euclidean distance in
n-dimensional spectral space
• D = SQRT[(sum (dk - ek)2]
where dk = BV of pixel d in band k
where ek = BV of pixel e in band k
• the equation is summed across k = 1 to n
bands
What is the spectral distance
between Pixel A and Cluster 1?
Pixel A
Y
92, 153
Cluster 1
180, 85
X
Spectral Distance example
Distance between [x1,y1] & [x2, y2]
[180, 85] & [92, 153]
D = SQRT[(sum (dk - ek)2]
D = SQRT[(180-92)2 + (85-153)2]
= SQRT[(88)2 + (-68)2]
= SQRT[7744 + 4624]
= SQRT[12,368] = 111.2
Spectral Distance example
Y
92, 153
Yd = 85-153
180, 85
Xd = 180 -92
X
Supervised vs. Unsupervised
Approaches
• Supervised - image analyst "supervises" the
selection of spectral classes that represent
patterns or land cover features that the analyst
can recognize
Prior Decision
• Unsupervised - statistical "clustering"
algorithms used to select spectral classes
inherent to the data, more computer-automated
Posterior Decision
Supervised vs. Unsupervised
Supervised Prior Decision: from Information classes in the
Image to Spectral Classes in Feature Space
N
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R
Red
Unsupervised Posterior Decision: from Spectral Classes in
Feature Space to Information Classes in the Image
Supervised
Select Training
fields
Edit/evaluate
signatures
Classify
image
Evaluate
classification
vs.
Unsupervised
Run clustering
algorithm
Identify
classes
Edit/evaluate
signatures
Evaluate
classification
ISODATA (Iterative Self-Organizing Data
Analysis Technique) Clustering Algorithm
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User specified Input (as implemented in ERDAS)
maximum number of clusters
Initial allocation of cluster center locations
maximum % of pixels whose class values are allowed to
be unchanged between iterations.
maximum number of iterations
Other possible constraints (not implemented in ERDAS)
minimum number of members in a cluster, if fall below
threshold then that cluster eliminated
maximum standard deviation: if the std dev exceeds the
threshold then that cluster is split into two
minimum distance between cluster means
Initial Cluster Allocation
• clusters allocated along the mean ndimensional vector
• spaced according to std dev distance away
from central mean
N
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Red
Algorithm Operation
• each pixel is compared to each cluster mean and
assigned to the cluster whose mean is closest in
Euclidean distance
________________________________
\/(DNb1i-DNb1m)2 + ... + (DNbxi - DNbxm)2))
and a new cluster center is computed by
averaging the locations of all the pixels assigned
to that cluster.
•ISODATA: multiple iterations from initial allocation to final
assignment
•The algorithm will stop either when the # iteration threshold is
reached Or the max % of unchanged pixel threshold is reached
Initial clusters
NI
R
Final clusters
NI
R
Red
Red
Adapted from Jensen 2nd ed, 1996
Example: ISODATA clustering
Initial clusters: 1 (10,10), 2 (20,20) 3 (30,20)
Unclassified Pixel (30,10)
1. Assigning unclassified pixels to cluster means
Distance between Unclassified Pixel
Cluster 1
Cluster 2
Cluster 3
Band 4
30-10 = 20
30-20 = 10
30-30 = 0
Band 5
10-10 = 0
10-20 = -10
10-20 = -10
Squared distance
Sum
Sqr Root
Band 4
400
100
0
400
200
100
20
14
10
Band 5
0
100
100
Unclassified Pixel Assigned to Cluster 3
2. Calculating new cluster means
- Cluster 1 & 2 unchanged
- Cluster 3 migrates to Band 4mean, Band 5mean = 30, 15
Example of Naturally Clustered Data
Green Vegetation
NIR
Senesced
Vegetation
Red
Adapted from Swain
Initial cluster
centers
N
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x
After 1st
iteration
x
Red
After 2nd
iteration
x
Red
x
Red
x
N
I
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x
N
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R
Final cluster
centers
x
N
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x
Red
Final Cluster Centers
Green Vegetation
X
NIR
X
Senesced
Vegetation
Red
Adapted from Swain
N
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Spectral Feature Space
Grass
Trees
Impervious
Surface &
Bare Soil
water
Red Reflectance
In spectral feature
space, generally no
distinct, isolated
clusters, rather a
continuous gradient.
Classification can be
thought of as trying to
subdivide the feature
space into appropriate
spectral regions
Algorithm Operation
• The objective is to minimize the Sum of Squared
Errors (SSE) across the entire set of clusters and
the original pixel data
• The Sum of Squared Errors (SSE): is the
cumulative squared difference (i.e., spectral
distance across all the various bands) of each
pixel from its assigned cluster center
• An optimal arrangement has been reached that
minimizes the distance between every pixel and a
cluster center (based on that number of clusters)
HWK Example: SSE calculation
Initial clusters (2,1) & (4,2) 1st iteration
X Y DistA DistB SSE
1
2 1.41
3.00
2.0
2
3 2.00
2.24
4.0
2
4 3.00
2.83
8.0
3 4 3.16
2.24
5.0
4
3 2.83
1.00
1.0
4
4 3.60
2.00
4.0
4 6 5.38
4.00
16.0
5
7 6.71
5.10
26.0
6
5
3.60
13.0
5.66
New cluster centers
(1.5, 2.5)
(4, 4.6)
S = 79
Post-clustering Assignment
• The analyst must then assign each spectral
cluster to an information class based on
available ancillary information (e.g., field
reference data, maps, aerial photos, analyst
experience)
Posterior Decision process
• If one and only one information class can
not be unequivocally assigned to a cluster
then assign the cluster to a “mixed” class
Post Clustering Assignment: what
information class can be assigned to
each spectral cluster?
71:grass
21:low intensity
residential
33:transitional
31:Bare rock
41:decidous
forest
NI
R
91:wetlands
42 evergreen
forest
11:water
32: Quarries
84:fallow
22: high intensity residential
23: Commercial
Red
Adapted from Jensen 2nd ed, 1996
ISODATA Clustering: Pros
• clustering not geographically biased to any
particular portion of the image
• highly successful at finding inherent spectral
clusters
• results similar to a minimum-distance-to-means
classifier
ISODATA Clustering: Cons
• analyst doesn’t know a priori number of
spectral classes
• number of iterations needed, can be time
consuming
• does not account for pixel spatial
homogeneity
• Insensitive to variance/covariance
Cluster-busting
• technique to iteratively “bust up” spectrally
“mixed” classes
• separate “good” vs. “bad” classified pixels into a
binary mask
• mask out the “good” image, extract the “bad”
image data and re-run the unsupervised process
• re-evaluate new clusters, keep good, toss out
“bad”, cluster bust again
• create final cluster map by using a GIS overlay
with a maximum dominate function
Cluster-busting: in feature space
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N
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Red
Red
Cluster-busting: in geographic space
- separate “good” vs. “bad” classified pixels into a binary mask
- mask out the “good” (green) image
- extract the “bad”(red) image data
- re-run the unsupervised process
Cluster-busting
- re-evaluate new clusters, keep good, toss out “bad”, cluster bust
again (if needed)
- create final cluster map by using a GIS overlay with a maximum
dominate function
Overlay
Cluster busting
Recode
“good” class(es) = 0
“bad” class(es) > 1
Mask original image file
Cluster busting
New clusters
= the holes
Old clusters – “bad”
= swiss cheese
Overlay