INTRODUCTION TO DIGITAL IMAGE CLASSIFICATIONWAN BAKXADAPTED BY GABRIEL PARODI

Main lecture topics Review of basic concepts of pixel-based classification Review of principal terms (Image space vs. feature space) Decision boundaries in feature space Unsupervised vs. supervised classification Training of classifier Classification algorithms available Validation of results Problems and limitations

PURPOSE OF LECTURE

What is it ? grouping of similar features separation of dissimilar ones assigning class label to pixels resulting in manageable size of classes

MULTISPECTRAL CLASSIFICATION

Generalised workflow

Primary Data Acquisition Pre-processing Image restoration, Radiometric

corrections, Geometric corrections Image Enhancement Contrast, Noise, Sharpness

Image Fusion Multi-temporal, Multi-resolution,

Mosaicking Feature Extraction, quantitative Spectral (NDVI), Spatial (lines,

edges), Statistical (PCA) Information extraction, qualitative Classification

Supervised Unsupervised

Segmentation, spatial objects Visual Interpretation

What are the advantages of using image classification?

We are not interested in brightness values, but in thematic characteristics

To translate continuous variability of image data into map patterns that provide meaning to the user

To obtain insight in the data with respect to ground cover and surface characteristics

To find anomalous patterns in the image data set

MULTISPECTRAL CLASSIFICATION

Why use it? - cont’ Cost efficient in the analyses of large data sets Results can be reproduced More objective then visual interpretation Effective analysis of complex multi-band (spectral)

interrelationships Classification achieves data size reduction

MULTISPECTRAL CLASSIFICATION

Together with manual digitising and photogrammetricprocessing (for map making), classification is the mostcommonly used image processing technique

Objective: Converting imagedata into thematic data

SUPERVISED CLASSIFICATION

Multi-band Image

IMAGE SPACE

One-dimensional feature space

Input layer (single)

Distinction between slices/classes

Histogram

Segmented image

unsupervised classification

No distinction between slices/classes

Histogram ?

MULTI-DIMENSIONAL FEATURE SPACE

statistical pattern recognition

feature vectors e.g.(34, 25, 117)(34, 24, 119)

FEATURE SPACE (SCATTERPLOT)

Feature space Two/three dimensional

graph or scattered diagram

Formation of clusters of points representing DN values in two/three spectral bands

Each cluster of points corresponds to a certain cover type on ground (theoretically)

High frequency

Low frequency

1D

DISTANCES AND CLUSTERS IN FEATURE SPACE

(0,0) band x (units of 5 DN)

band y (units of 5 DN)

. .

Euclidian distance

Min y

Max y

..(0,0) Min x Max x

. .... ..

Cluster

Supervised classification procedure

1. Prepare Define/describe the classes,

define image criteria Aquire required image data

2. Define clusters in the feature space Collect ground truth Create a sample set

3. Choose a classifier / decisionrule / algorithm

4. Classify5. Validate the result

Application dependent aspects:• Class definition• Spatio-temporal characteristics

Sensor characteristics:• Bands• Spatial resolution• Acquisition date(s)

Sensor(s)

Band selection constraints:• Non correlated set• Software limitations

CLASSIFICATION PREPARATION

UNSUPERVISED APPROACH Considers only spectral distance measures Minimum user interaction Requires interpretation after classification Based on spectral groupings

SUPERVISED APPROACH Incorporates prior knowledge Requires a training set (samples) Based on spectral groupings More extensive user interaction

SUPERVISED VS. UNSUPERVISED CLASSIFICATION

UNSUPERVISED SLICING

Input layer (single)

Distinction between slices

Histogram

Segmented image

unsupervised classification

Unsupervised classification (clustering)

Clustering algorithm User defined cluster parameters Class mean vectors are arbitrarily set

by algorithm (iteration 0) Class allocation of feature vectors Compute new class mean vectors Class allocation (iteration 2) Re-compute class mean vectors Iterations continue until convergence

threshold has been reached Final class allocation Cluster statistics reporting

Recode/group them into sensible classese.g. 2, 3, 4 and 5 make one class

Feature spaces!

Supervised Classification

Principle Collect samples for training

the classifier Define clusters (decision

boundaries) in the feature space

Assign a class label to a pixel based on its feature vector and the predefined clusters in the feature space

(160,170)(160,170) = Grass

(60,40)(60,40)= House

TRAINING SAMPLE STATISTICS

E.g. Minimum, Maximum, Mean, Standard deviation, Variance, Co-Variance

The points a,b and c are cluster centres of clusters A, B and C. Line ab is the distance between the cluster centres A and B.

There is overlap between the clusters A and B.

TRAINING SAMPLES IN POTENTIAL FEATURE SPACES

SAMPLE SET - 1 BAND

0 31 63 95 127 159 191 223 255

Freq.

300

200

100

0

Ground-truth

Class-Slices

Histogram of training/sample set

Samples setof classes

1 BAND/DIMENSION - SLICING

0 31 63 95 127 159 191 223 255

300

200

100

0

Class-Intervals

Histogram of training set

Decision rule:

Priority to the smallest slice length/spreading

0 255

0

255

Band 1

Means and Standard Deviations

0 255

0

255

Band 2

Band 1

Feature Space Partitioning - Box classifier[Min,Max] or [Mean - xSD,Mean + xSD]

Partitioned Feature Space

Band 2

TWO BANDS – BOX CLASSIFICATION

Box classification

Characteristics considers only the lower

and the upper limits of cluster

computation is simple and fast

Disadvantage overlapping boxes poorly adapted to

cluster shape

1 DIMENSION - MINIMUM DISTANCE

0 31 63 95 127 159 191 223 255

300

200

100

0

Class-Intervals

Histogram of training set

Decision rule:

Priority to the shortest distance to the class mean

Feature Space Partitioning - Minimum Distance to Mean Classifier

0

255

0 255

Band 2

Band 1

0255

Band 2

Band 1

Mean vectors

0

255

"Unknown"

2550

255

Band 2

Band 10

Threshold Distance

N DIMENSIONS – MIN. DISTANCE TO MEAN

Minimum distance to mean classifier

Characteristics emphasis on the location of

cluster centre class labelling by

considering minimum distance to the cluster centres

Disadvantage disregards the presence of

variability within a class shape and size of the

clusters are not considered

0 31 63 95 127 159 191 223 255

300

200

100

0

1 BAND – MAXIMUM LIKELIHOOD

Class-Intervals

Histogram of training set &Probability density functions

Priority to the highest probability (based upon σ and μ)

2

2

2σμ)(x

e2πσ

1f(x)

The probability that a pixel value x belongs to a class is calculated assuming a normal/Gaussian distribution

Decision rule:

255

0255

Band 2

Band 10

02550

255

Band 2

Band 1

Feature Space Partitioning -Maximum Likelihood Classifier

0 255

Band 2

Band 10

255

"Unknown"Mean vectors and variance-covariance matrices

MAXIMUM LIKELIHOOD CLASSIFIER

MAXIMUM LIKELIHOOD CLASSIFCATION

Characteristics considers variability within a

cluster considers the shape, the size

and the orientation of clusters

Disadvantage takes more computing time based on assumption that

Probability Density Function is normally distributed

Equiprobability contours

Probability density functions (Lillesand and Kiefer, 1987)

Systematic Sampling (n=9) Simple Random Sampling (n=9) Stratified Random Sampling (n=9)

C

A B

C

A B

C

A B

VALIDATION – SAMPLING SCHEME

Number of samples is related to: The number of samples that must be taken in order to reject a data

set as being inaccurate The number of samples required to determine the true accuracy,

within some error bounds

Sampling design:

TotalA B C D

A 35 14 11 1 61

B 4 11 3 0 18

C 12 9 38 4 63

D 2 5 12 2 21

Total 53 39 64 7 163

Reference ClassC

lass

ificat

ion

Res

ult

ACCURACY ASSESSMENT

Basic data for 4 land cover classes 163 ground truth samples

Reference or Ground Truth ≠ Sample/training set

MEASURES OF THEMATIC ACCURACY

Error of commission and user accuracy Error of omission and producer accuracy

Error or confusion matrix

Total

A B C D

A 35 14 11 1 61 43% 57%

B 4 11 3 0 18 39% 61%

C 12 9 38 4 63 40% 60%

D 2 5 12 2 21 90% 10%

Total 53 39 64 7 163

34% 72% 41% 71% Overall Accuracy = SumDiag/SumTotal(4+12+2)/53 . . . . . . . . . 53%

66% 28% 59% 29%35/53 . . . . . . . . .

Producer Accuracy

Cla

ssifi

catio

nre

sult

Error ofCommision

UserAccuracy

Error of Omission

Reference Class

VALIDATION

Total

A B C D

A 35 14 11 1 61 43 57%

B 4 11 3 0 18 39 61%

C 12 9 38 4 63 40 60%

D 2 5 12 2 21 90 10%

Total 53 39 64 7 163

34 72 41 71 Overall Accuracy = SumDiag/SumTotal(4+12+2)/53 . . . . . . . . . 53%

66% 28% 59% 29%35/53 . . . . . . . . .

Producer Accuracy

Cla

ssifi

catio

nre

sult

Error ofCommision

UserAccuracy

Error of Omission

Reference Class

Row : ClassificationError of Commission = Reliability = ∑Row_offdiagonal/ ∑Row

Column : ReferenceError of Omission = Accuracy/class = ∑Col_offdiagonal/ ∑Col

User accuracy: Probability that a certain reference class has also been labelled as 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 class. It

indicates how well the reference pixels for that class have been classified (66% of the reference pixels A were classified as A)

Kappa statistic: Takes into account that even assigning labels at random has a certain

degree of accuracy. Kappa allows to detect if 2 datasets have a statistically different accuracy.

VALIDATION – TERMINOLOGY

The error matrix provides information on the overall accuracy = proportion correctly classified (PCC)

PCC tells about the amount of error, not where the errors are located

PCC = Sum of the diagonal elements/total number of sampled pixels for accuracy assessment

ERROR MATRIX

Create more than 1 feature class for one land cover/use class Filter salt/pepper (majority on result) Use masks to identify areas where other rules apply (hybrid) Use multi temporal expertise to identify classes (expert

knowledge) Use other additional data (expert knowledge)

IMPROVEMENTS

No use of other characteristics location, orientation, pattern, texture . . .

Single class label per pixel

Spectral overlap Heterogeneous classes Mixed pixels (boundaries)

Class definition Land Use Land Cover

PIXEL BASED PROBLEMS

Constraints of pixel based image classification it results in spectral classes each pixel is assigned to one class only

Spectral bands - Spectral classes - Land cover - Land use

Land cover

Grass

Training samplesSpectral classes

Meadow

Land use

Sport

PROBLEMS – LAND COVER/LAND USE

DEM or other additional datacan help improve a classification

Spectral Class Land Cover Class Land Use Classwater water shrimp cultivationgrass1grass2grass3bare soil

grassgrassgrassbare soil

nature reservenature reservenature reservenature reserve

trees1trees2trees3

forestforestforest

nature reserveproduction forestcity park

1-n and n-1 relationships can existbetween land cover and land use classes

PROBLEMS – LAND COVER/LAND USE

Objects smaller than a pixel Mixtures

Boundaries between objects Transitions

PROBLEMS – MIXED PIXELS

PROBLEMS – SPATIAL RESOLUTIONResolution dependency

Large cluster in the feature space

Spectral overlap with other classes

Distinct reflection measurement

Each pixel contains approximately the same mixture

Regular, repetition of spatial pattern

Object Based Classification

Hybrid (stratified) Classification Unsupervised/Clustering (Hyper)Spectral Classifications Subpixel Classification Expert/Knowledge Based Classification Neural Network

ALTERNATIVE PROCEDURES

EXAMPLE - FEATURE SPACE

BOX CLASSIFICATION FACTOR 4

BOX CLASSIFICATION FACTOR 10

MINIMUM DISTANCE THRESHOLD 50

MINIMUM DISTANCE THRESHOLD 100

MAXIMUM LIKELIHOOD THRESHOLD 100

Image

Pixel Basedclassification

Segmentation

Object classification Majority based

Object classification

Classifiedsegments Assessment

Assessment

OBJECT BASED CLASSIFICATION (ADV.)

IHE: Introduction to Remote Sensing with applications in water resources

Objects

Obtain objects by: Edge detection Post-classification Segmentation Vector reference

CLASSES

Obtain class label from: Frequency/majority Object mean . . .

OBC BY OBJECT MEANS

Image tiv )

Segmentation pixels segmentivalue = μ (segmenti)

class signatures Retrieve

class signatures

Assessment

Trainingsamples

Classify segmentsClassify segments