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Comparison of Pixel and Object Oriented Comparison of Pixel and Object Oriented based Classification of Fused Imagesbased Classification of Fused Images

Dr. M. Seetha

Professor, Dept. of CSE.,

GNITS, Hyderabad-08

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OUTLINE

• Introduction to Image Fusion

• Image Data

• Image Classification – Pixel based Image Classification– Object Oriented Classification

• Accuracy Assessment Measures

• Discussion of Results

• Conclusions

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Introduction to Image Fusion

• Extract maximal information so as to achieve optimal resolution in the spatial and spectral domains.

• Process of combining two or more source images into a single composite image with extended information contained.

• The fused image should have more complete information which is more useful for human or machine perception.

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Image Data

• Two data sets were collected via IRS 1D satellites using LISS III sensors in both the panchromatic (PAN) mode and multispectral (MS) mode by NRSA, Hyderabad, Andhra Pradesh (AP), INDIA.

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• Multispectral image and panchromatic images of Khammam -27th November 2002, having the path - row combination as 101 – 060 from the IRS 1D LISS III sensor at time 05:19:50. 576 x 726 -MS and 1152 x 1452 -PAN.

• Multispectral and panchromatic images of the Hyderabad city, AP, INDIA, are acquired on, 18th February 2001, with path–row combination as 100-060 from the IRS 1D LISS III sensor at 05:40:44.

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Image Fusion Techniques

• Principal component Analysis

• Multiplicative method

• Brovey Transform

• Wavelet Transform Method

• Lifting Wavelet Transform Method

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LISS III , PAN and Fused Images of Data Set 1

LISS III PAN Brovey

Multiplicative PCA Wavelet

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IMAGE CLASSIFICATION

• To label the pixels in the image with meaningful information of the real world.

• Classification of complex structures from high resolution imagery causes obstacles due to their spectral and spatial heterogeneity.

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• The fused images obtained by different fusion techniques alter the spectral content of the original images.

• Therefore, the spectral separabiltiy of the classes was analyzed by the classification of fused images.

• The classification accuracy of the original multispectral and fused images was assessed with parameters of overall accuracy and kappa statistic.

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Pixel-Based image classification

• Based on pixels and classification manner is

pixel-by-pixel.

• Uses hard classifiers

• Two types

– Unsupervised classification

– Supervised classification

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Supervised vs. Unsupervised Approaches

– Unsupervised - statistical "clustering" algorithms

used to select spectral classes inherent to the data,

more computer-automated

Posterior Decision

– Supervised - image analyst "supervises" the selection

of spectral classes that represent patterns or land

cover features that the analyst can recognize

Prior Decision

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Supervised vs. Unsupervised

Edit/evaluate signatures

Select Training fields

Classify image

Evaluate classification

Identify classes

Run clustering algorithm

Evaluate classification

Edit/evaluate signatures

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K-Means Classifier

• Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.

• Assign each object to the group that has the closest centroid.

• When all objects have been assigned, recalculate the positions of the K centroids.

• Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized can be calculated.

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Maximum Likelihood Classifier

Band 2 Digital Number

Band 1 Digital Number

Based on a normalized (Gaussian) estimate of the probability density function of each class. Quantitatively evaluates both variance and covariance of the category spectral response patterns while classifying an unknown pixel.

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Object-Oriented Image Classification

• Used objects for classification

• Uses soft classifiers

• Two steps involved

– Segmentation

– Fuzzy classification

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Segmentation

• Divide into different regions

• Basic task is merge image elements

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Hierarchical network of image segmentation

Level-3

Level-2

Level-1

Pixel Level

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Fuzzy Classification for OOIC

• classifier is soft classifier

– example fuzzy system

• membership value lies between 1.0 to 0.0

• Advantages

– to express uncertainties about the classes descriptions

– to express each object’s membership in more than just one class

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Accuracy Assessment Measures

• Error Matrix

– is a square, with the same number of information classes which will be assessed as the row and column.

Overall accuracy (OA)=

• Kappa coefficient

KK

KK

iK

N

NK=1

NK=1

i,K=1

1a

aa n

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The Error Matrix

Reference DataClass 1 Class2 … Class N Row Total

Class 1

Class 2

Class N

… … … …

a2N

a1Na12

a22

a11

a21

aN1 aN2 aNN

Classifica- -tion Data

Column Total

K2

N

K=1

a

1K

N

K=1

a

2K

N

K=1

a

2K

N

K=1

a

K1

N

K=1

a KN

N

K=1

a iK

N

i,K=1

N a

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Kappa coefficient

Khat = (n * SUM Xii) - SUM (Xi+ * X+i) n2 - SUM (Xi+ * X+i)

where SUM = sum across all rows in matrix

Xi+ = marginal row total (row i)

X+i = marginal column total (column i)

n = # of observations takes into account the off-diagonal elements of the contingency matrix (errors of omission and commission)

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Discussion of Results

• A comparative study of the results of pixel based and objects oriented image classification techniques.

• Object oriented image classification had more accurate results than the existing traditional pixel based techniques of unsupervised and supervised classification.

• Lifting Wavelet based on the object-oriented classification produced highest overall accuracy and kappa statistic.

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Overall accuracy of unsupervised, supervised and object oriented classification of LISS III and fused images for

data set 1

OA Vs Image fusion technique of data set 1

0

20

40

60

80

100

LISS-III BV MUL PCA WV LWT

Image fusion technique

OA

OA(UnSup)

OA(Sup)

OA(OOC))

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Kappa statistic of unsupervised, supervised and object oriented classification of LISS III and fused

images for data set 1 KS Vs Image fusion technique of data set 1

00.10.20.30.40.50.60.70.8

LISS-III BV MUL PCA WV LWT

Image fusion technique

KS

KS(Unsup)

KS(Sup)

KS(OOC)

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Spectral information of Unsupervised classified LISS-III and Fused images for data set 1

Water AgriculturalField

Greenery

Field

OpenArea

Urban

LISS-III 2904 18874 23713 17718 2327

Brovey 110749

356033 362083 264162

75338

PCA 99557 341087 313925 306361

108530

Multiplicative 147873

339501 346268 239472

95251

Wavelet 202616

295702 331172 248375

170071

Lifting Wavelet

503474

709113 634587 555459

396727

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Spectral information of supervised classified LISS-III and fused images for data set 1

Water AgriculturalField

Greenery

Field

OpenArea

Urban

LISS-III 2204 22168 35628 12 5524

Brovey 236321 22026 546918 55166 307934

PCA 17600 566300 99900 410900

71600

Multiplicative 61583 283473 620809 40415 162085

Wavelet 744662 2614 9061 7388 484211

Lifting Wavelet

1152847

498085 33443 5029 1109956

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Spectral information of object oriented classified LISS-III and fused images for data set 1

Water AgriculturalField

GreeneryField

OpenArea

Urban

LISS-III 2223 22284 35478 9 5542

Brovey 281248 279674 113783 487539 147616

PCA 19529 568989 97460 409063 74419

Multiplicative 61583 283473 620809 40415 162085

Wavelet 718916 4781 33411 27422 718916

Lifting Wavelet 1067466 653692 76919 17990 983293

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• Image segmentation is significant in object oriented image analysis and aptly selected segmentation parameters influence the classification results.

• It is apparent that object oriented classification based on segmentation enhanced classification accuracy results.

• Lifting Wavelet with object-oriented classification produced highest overall accuracy and kappa statistic.

Conclusions

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THANK YOU