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1
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
2
OUTLINE
• Introduction to Image Fusion
• Image Data
• Image Classification – Pixel based Image Classification– Object Oriented Classification
• Accuracy Assessment Measures
• Discussion of Results
• Conclusions
3
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.
4
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.
5
• 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.
6
Image Fusion Techniques
• Principal component Analysis
• Multiplicative method
• Brovey Transform
• Wavelet Transform Method
• Lifting Wavelet Transform Method
7
LISS III , PAN and Fused Images of Data Set 1
LISS III PAN Brovey
Multiplicative PCA Wavelet
8
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.
10
Pixel-Based image classification
• Based on pixels and classification manner is
pixel-by-pixel.
• Uses hard classifiers
• Two types
– Unsupervised classification
– Supervised classification
11
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
12
Supervised vs. Unsupervised
Edit/evaluate signatures
Select Training fields
Classify image
Evaluate classification
Identify classes
Run clustering algorithm
Evaluate classification
Edit/evaluate signatures
13
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.
14
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.
15
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
17
Hierarchical network of image segmentation
Level-3
Level-2
Level-1
Pixel Level
18
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
19
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)
22
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.
23
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))
24
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)
25
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
26
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
27
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
29
THANK YOU