1. 1 Comparison of Pixel and Object Oriented based Classification of Fused Images Dr. M. Seetha Professor, Dept. of CSE., GNITS, Hyderabad-08

2. 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. 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. 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. 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. 6 Image Fusion Techniques Principal component Analysis Multiplicative method Brovey Transform Wavelet Transform Method Lifting Wavelet Transform Method

7. 7 LISS III, PAN and Fused Images of Data Set 1 LISS III PANBrovey Multiplicative PCAWavelet

8. 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.

9. 9 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. 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. 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. 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. 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. 14 Maximum Likelihood Classifier Band 2 Digital Number B a n d 1 Di gi ta l N u m be r 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. 15 Object-Oriented Image Classification Used objects for classification Uses soft classifiers Two steps involved –Segmentation –Fuzzy classification

16. 16 Segmentation Divide into different regions Basic task is merge image elements

17. 17 Hierarchical network of image segmentation Level-3 Level-2 Level-1 Pixel Level

18. 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. 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

20. 20 The Error Matrix Reference Data Class 1Class2 … Class NRow Total Class 1 Class 2 Class N … … …… a 2N a 1N a 12 a 22 a 11 a 21 a N1 a N2 a NN Classifica- -tion Data Column Total

21. 21 Kappa coefficient K hat = (n * SUM X ii ) - SUM (X i+ * X +i ) n 2 - SUM (X i+ * X +i ) where SUM = sum across all rows in matrix X i+ = 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. 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. 23 Overall accuracy of unsupervised, supervised and object oriented classification of LISS III and fused images for data set 1

24. 24 Kappa statistic of unsupervised, supervised and object oriented classification of LISS III and fused images for data set 1

25. 25 Spectral information of Unsupervised classified LISS-III and Fused images for data set 1 WaterAgricultural Field Greenery Field Open Area Urban LISS-III29041887423713177182327 Brovey11074935603336208326416275338 PCA99557341087313925306361108530 Multiplicative14787333950134626823947295251 Wavelet202616295702331172248375170071 Lifting Wavelet503474709113634587555459396727

26. 26 Spectral information of supervised classified LISS- III and fused images for data set 1 WaterAgricultural Field Greenery Field Open Area Urban LISS-III22042216835628125524 Brovey2363212202654691855166307934 PCA176005663009990041090071600 Multiplicative6158328347362080940415162085 Wavelet744662261490617388484211 Lifting Wavelet11528474980853344350291109956

27. 27 Spectral information of object oriented classified LISS-III and fused images for data set 1 WaterAgricultural Field Greenery Field Open Area Urban LISS-III2223222843547895542 Brovey281248279674113783487539147616 PCA195295689899746040906374419 Multiplicative6158328347362080940415162085 Wavelet71891647813341127422718916 Lifting Wavelet10674666536927691917990983293

28. 28 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. 29 THANK YOU