l7 digital classification slides bb(1)
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Introduction to digital imageclassification
Wan Bakx2009
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
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Pur ose of lecture
Main lecture topicsReview of basic conce ts of ixel-basedclassificationReview of principal terms (Image space vs. featurespace)
Decision boundaries in feature spaceUnsupervised vs. supervised classificationTraining of classifier
Classification algorithms availableValidation of results
Problems and limitations
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The Remote Sensin ProcessSatComSensor
pp ca on
Target
Processing
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Multis ectral Classification
What is it ?
separation of dissimilar onesassigning class label to pixels
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Generalised workflow
Primary Data AcquisitionPre-processing
Image restoration, Radiometric,
correctionsImage Enhancement
Contrast Noise Shar nessImage Fusion
Multi-temporal, Multi-resolution,Mosaicking
Feature Extraction , quantitativeSpectral (NDVI), Spatial (lines,edges), Statistical (PCA)
,
ClassificationSupervised
Segmentation, spatial objectsVisual Interpretation
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Multis ectral Classification
What are the advantages of using image
We are not intereste in rig tness va ues, ut in
thematic characteristicsTo translate continuous variability of image datainto map patterns that provide meaning to the user
To obtain insight in the data with respect to groundcover and surface characteristicsTo find anomalous patterns in the image data set
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Multis ectral Classification
Why use it? - cont
Results can be reproducedMore objective then visual interpretation
-(spectral) interrelationships
Classification achieves data size reductionTogether with manual digitising and photogrammetricprocessing (for map making), classification is the most
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Su ervised Classification
Objective: Converting imagedata into thematic data
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Ima e S ace
Multi-band Image
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One-dimensional feature s ace
Input layer (single)
Segmented imageNo distinction between slices/classes
Histogram
Distinction between slices/classes
unsupervised classification
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Multi-dimensional Feature S ace
feature vectors e.g.(34, 25, 117 )34 24 119
statistical pattern recognition
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Feature s ace scatter lot
Feature spaceTwo/threedimensional graph orscattered diagram
Low frequency
Formation of clustersof points representingDN values intwo/three spectralbands
Each cluster of pointscorresponds to acertain cover type onground (theoretically)
High frequency
1D
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Distances and clusters in feature space
band y(units of 5 DN) .
. .
..
(0,0) band x (units of 5 DN) Min y... .
Euclidian distance (0,0) Min x Max x
Cluster
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Classification rocedure
1. PrepareDefine/describe the classes,define image criteria
Aquire required image data2. Define clusters in the
Define classes Text
Collect ground truth
Create a sample set
Truth Digital data
Digital
Satisfied
Y /N
N
. oose a c ass erdecision rule / algorithm
Choose decision
samples
Quality Assessment
Accuracymatrix
5. Validate the result
rule
Classify Image
Classification
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Classification re aration
Sensor characteristics:
Class definition Spatio-temporal characteristics
an s Spatial resolution
Acquisition date(s)
Band selection constraints: Non correlated set
Sensor(s)
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Supervised vs. unsupervised classification
UNSUPERVISED APPROACH
Minimum user interactionequ res n erpre a on a er c ass ca on
Based on spectral groupings
Incorporates prior knowledge
Based on spectral groupingsMore extensive user interaction
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Unsu ervised Slicin
Input layer (single)
Segmented image
Histo ram
unsupervised classification
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Unsupervised classification (clustering)
Clustering algorithmUser defined cluster parameters
set by algorithm (iteration 0)Class allocation of feature vectors
ompu e new c ass mean vec orsClass allocation (iteration 2)Re-com ute class mean vectorsIterations continue untilconvergence threshold has been
Final class allocationCluster statistics reporting
Recode/group them into sensible
classese.g. 2, 3, 4 and 5 make one class ea ure spaces
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Su ervised Classification
PrincipleCollect samples fortrainin the classifier
Define clusters(decision boundaries)in the feature s ace Assign a class label toa pixel based on its
the predefinedclusters in the feature
160,170160,170 = Grass
(60,40)(60,40)= House
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Trainin sam le statistics
E.g. Minimum, Maximum, Mean, Standard deviation,ar ance, o- ar ance
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Training samples in potential feature spaces
The points a,b and care cluster centres of There is overlap
between the
Line ab is thedistance between the
clusters A and B.
B.
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Sam le set - 1 Band
Freq.Ground-truthHistogram of training/sample set
300
100
0 31 63 95 127 159 191 223 255
0
Class-Slices
Samples setof classes
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1 band/dimension - Slicin
Histogram of training set
300
200
100
0 31 63 95 127 159 191 223 255
0
Class-Intervals
Decision rule:
Priority to the smallest slice length/spreading
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Two bands Box Classification
255
Means and Standard Deviations
255
Partitioned Feature Space
Band 2 Band 2
0 255
0
Band 1 0 255
0
Band 1
-[Min,Max] or [Mean - xSD,Mean + xSD]
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Box classification
Characteristics
the upper limits of cluster
fast
Disadvantage
overlapping boxesoorl ada ted to cluster
shape
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1 Dimension - Minimum Distance
Histogram of training set
300
200
100
0 31 63 95 127 159 191 223 255
0
Class-Intervals
Decision rule:
Priority to the shortest distance to the class mean
d
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N dimensions Min. Distance to Mean
255
"Unknown"
Band 2Mean vectors255
00 255Band 1Band 2
255
255Band 10
Band 2
ea ure pace ar on n - n mumDistance to Mean Classifier
2550
Band 10
Threshold Distance
Mi i di l ifi
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Minimum distance to mean classifier
Characteristicsemphasis on the location of
c us er cen reclass labelling by considering
cluster centres
Disadvantagedisre ards the resence of
variability within a classshape and size of the clustersare no cons ere
1 b d M i Lik lih d
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1 band Maximum Likelihood
Histogram of training set &Probability density functions
300
200
The probability that a pixelvalue x belongs to a class is
100
calculated assuming anormal/Gaussian distribution
0 31 63 95 127 159 191 223 255
0
2
2
2
)(x1
=
Class-Intervals2
Priority to the highest probability (based upon and )
Decision rule:
M i lik lih d l ifi
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Maximum likelihood classifier
Band 2
"Unknown"Mean vectors and variance-
255
02550 Band 1Band 2
255
Band 2
-
0 255Band 1
0255Band 10
Maximum Likelihood Classifier
M i Lik lih d l if ti
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Maximum Likelihood classifcation
Characteristicsconsiders variabilit within a
qu pro a ty contours
clusterconsiders the shape, the sizeand the orientation of clusters
Disadvantagetakes more computing timebased on assumption thatProbability Density Functionis normally distributed
Probability density functions (Lillesand and Kiefer, 1987)
Validation sam lin scheme
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Validation sam lin scheme
Number of samples is related to:The number of sam les that must be taken in order toreject a data set as being inaccurate
true accuracy, within some error bounds
Sampling design:
C C C
Systematic Sampling (n=9) Simple Random Sampling (n=9) Stratified Random Sampling (n=9)
Accurac assessment
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Accurac assessment
163 ground truth samples
Total
A B C D
Reference Class
B 4 11 3 0 18
C 12 9 38 4 63 a s s
i f i c a
t i o n
R
e s u
l t
D 2 5 12 2 21
Total 53 39 64 7 163
C l
Reference or Ground Truth Sample/training set
Measures of thematic accurac
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Measures of thematic accurac
Error of commission and user accuracyError of omission and roducer accurac
Total Error of Commision
UserAccurac
Reference Class
A B C D
A 35 14 11 1 61 43% 57%
B 411
3 0 18 39% 61% i c a t i o n
s u
l
C 12 9 38 4 63 40% 60%
D 2 5 12 2 21 90% 10%
Total 53 39 64 7 163
C l a s s i
r e
34% 72% 41% 71% Overall Accuracy = SumDiag/SumTotal
(4+12+2)/53 . . . . . . . . . 53%
66% 28% 59% 29%Producer Accurac
Error of Omission
35/53 . . . . . . . . .
Validation
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Validation
Total Error of UserReference Class
A B C D
A 35 14 11 1 61 43 57%
B 4 11 3 0 18 39 61% i f i c a t i o n
s u
l t
Commision Accuracy
C 12 9 38 4 63 40 60%
D 2 5 12 2 21 90 10%
Total 53 39 64 7 163
C l a s s i
r
(4+12+2)/53 . . . . . . . . . 53%66% 28% 59% 29%
35/53 . . . . . . . . .
Producer Accuracy
Row : Classification_
Column : ReferenceError of Omission = Accuracy/class = Col_offdiagonal/ Col
Validation terminolo
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Validation terminolo
User accuracy:Probability that a certain reference class has also been labelledas that class. In other words it tells us the likelihood that a
pixel classified as a certain class actually represents that class(57% of what has been classified as A is A).
Producer accuracy:Probability that a reference pixel on a map is that particular
.have been classified (66% of the reference pixels A wereclassified as A)
Kappa statistic:Takes into account that even assi nin labels at random has acertain degree of accuracy. Kappa allows to detect if 2 datasetshave a statistically different accuracy.
Error matrix
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Error matrix
The error matrix provides information on theoverall accuracy = proportion correctly
classified (PCC)
PCC tells about the amount of error, not
PCC = Sum of the diagonal elements/totalnumber of sampled pixels for accuracyassessment
Im rovements
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Im rovements
Create more than 1 feature class for one
Filter salt/pepper (majority on result)Use masks to identify areas where other
Use multi temporal expertise to identifyc asses exper now e ge
h r ddi i n d x r knowledge)
Pixel based roblems
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Pixel based roblems
No use of other characteristicslocation, orientation, pattern, texture . . .
Single class label per pixel
Spectral overlap
Mixed pixels (boundaries)
Land Use
Problems Land Cover/Land Use
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Problems Land Cover/Land Use
it results in spectral classeseach pixel is assigned to one class only
Land use
Sport
Grass
Training samplesSpectral classes
Spectral bands - Spectral classes - Land cover - Land use
Problems Land Cover/Land Use
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Problems Land Cover/Land Use
water water shrimp cultivationgrass1 grass nature reserve
grass3 bare soi l
grassgrass
bare soil
na ure reservenature reservenature reserve
trees2 trees3
forestforest
production forestcity park
1-n and n-1 relationships can existbetween land cover and land use classes
DEM or other additional datacan help improve a classification
Problems mixed ixels
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Problems mixed ixels
Objects smaller than a pixel
Boundaries between ob ectsTransitions
Problems s atial resolution
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Problems s atial resolution
Resolution de endenc
Each pixel containsapproximately the same mixture
Distinct reflection measurement
Large cluster in the feature space
Regular, repetition
pec ra over ap w o erclasses
Alternative rocedures
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te at ve ocedu es
Object Based Classification
Unsupervised/Clustering(Hyper)Spectral Classificationsu p xe ass cat on
Neural Network
Exam le - Feature s ace
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Box classification factor 1.7
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Box classification factor 4
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Box classification factor 10
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Minimum distance threshold 50
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Minimum distance threshold 100
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Maximum likelihood threshold 100
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Ob ect Based Classification adv.
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Segmentation Classifiedse ments Assessment
Ima e Object classification
Pixel Basedclassification Assessment
Ob ects
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Obtain objects by:
ge e ec on-SegmentationVector reference
Classes
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Obtain class label from:
requency ma or y . . .
OBC by object means
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SegmentationImage pixels segment i
value = (segment i)
AssessmentClassify segments
class signaturessamples