l7 digital classification slides bb(1)

7/27/2019 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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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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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

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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%

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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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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



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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

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