road extraction by using SVM-based approaches---please contact with abyssecho@hotmail.com if you have any idea to share.

1. Road Extraction fromRemote Sensing Images using SVM combined with FCM and MRF Jiawei Xu MULTIMEDIA PROCESSING LABORATORYDepartment of Electronic Engineering, Hallym UniversityE-mail: abyssecho@hallym.ac.kr

2. Contents Introduction A brief review of previous methods Multi-times SVM Proposed algorithm Experimental results Performance evaluation Conclusion

3. A brief review of previous methods Mathematical morphology Hough transform P-value segmentation Genetic algorithm Markov random field

4. MATHEMATICAL MORPHOLOGY Flowchart of the proposed algorithm in mathematical morphology Original image 2D median filter open operator erosion & thinning Structure element：square, rectangle, ball, disk and line… Morphology: Erosion，dilation，openoperator, close operator

5. Hough transform The Hough transform is most commonly used for the detection of regular curves such as lines, circles, ellipses, etc. Parameter setting in this thesis: Detection sensitivity: 0.15 The smaller the value is, the more features in the image will be considered as lines.

6. P-value segmentation This figure shows the entire procedure of road extraction using P value segmentation. In figure (a) is an input image with some selected road parts on it, (b) is the cumulative gray-image histogram distribution , (c) is the result of P value segmentation. Application of some morphological operators such as region property, open operator to (c) results in (d), (e) is the road net image using thinning function, (f) shows the input image with the extracted road net overlaid on it

7. P-value segmentation Post processing incurs an information loss. (a) is the original image, (b) is the P value diagram, (c) is the result of P value segmentation and (d) is the application of some morphological operators, such as region property, open operator based on (c). Compare with Genetic algorithm threshold segmentation Best fitness and best threshold with generation increase

8. Markov random field Mean value and variance of selected area Original image MRF processing Speckle-like noise removal Road net

10. belong to a family of generalized linear classifiers

11. minimize the empirical classification error

13. To slove such a problem is to import a nonlinear mapping function. If data sets in lower dimensional space are transformed to a higher dimensional space and successfully separated by a linear hyperplane as we showed in right figure ,we can utilize SVM.linear optimal separating hyper-plane nonlinear optimal separating hyper-plane

16. Block diagram of our approach FCM classifer RS image SVM classifer MRF regularizer Road image

17. FCM preprocessor Algorithm Calculate the fuzzy cluster centers by using and the new partition matrix by using Update to 3. Stop iteration if otherwise set and return to step 2 non-road-like non-road-like road-like image1 image 2 image 3 Partition into a collection of c fuzzy clusters with a list of c cluster centers V , such that and a partition matrix where is a numerical value in [0,1] that tells the degree to which the element belongs to the i-th cluster.

18. FCM processing result (a) (b) (c) (d)(Road-like image used for further processing) (a)Original image(b)~(d)cluster 1,2,3

19. Why we use FCM before SVM? Because of unbalanced data If class A(non-road) samples distributed over a large area Class B(road) samples distributed over a small area, Supposing we use SVM directly, the coefficient Matrix will be more close to the class that has a large distribution (which means hyper-plane is close to the large-distributed class because of the property of SVM) An extreme case is : one-class SVM(to detect the outlier) If samples amounts is very few and distributed in an extremely small area, SVM will recognize it as outlier,(in road extraction,for extreme case, road part will be neglected as the outlier)i.e., the hyperplane is unbalancedly assigned, which will lead to misclassification. FCM can display the each cluster features more obviously,alternatively speaking, enlarge the difference/distance between different clusters, which more or less decrease the misclassification rate.

20. Test Images & Reference Models to evaluate our performance

21. Performance evaluation criteria Complete: Correct: Rank distance: Quality : True positive (TP): both the processed model and the reference scene model classify the pixel belonging to road. True negative (TN): both the processed model and the reference scene model classify the pixel as belonging to the background. False positive (FP): processed model classifies the pixel as belonging to road, but the reference scene model classifies the pixel as belonging to the background. False negative (FN): the processed model classifies the pixel as belonging to the background, but the reference scene model classifies the pixel as belonging to road

23. Shanghai.bmp

25. Experimental results We intentionally selected RS images with different characteristics：Beijing，shanghai，Vancouver… (ALL images from http://maps.google.com/) original images FCM-SVM processing MRF regularizer output images

26. Multi-classes SVM(one against all) RS image road-class image lake-class image expectation value initialization classification of samples to be recognized by SVM plot result image

27. Rank distance of K-means, SVM and our method (%) We do not list out morphology, Hough transform…because rank distance, quality percentage and other values are much lower than these approaches

28. Quality percentage of K-means, SVM and proposed method (%)

29. Comparison of FCM+K-means with FCM+SVM (a) Comparison of FCM+K-means with FCM+SVM (a) results of FCM clustering; (b) FCM followed by K-means clustring; (c) results of FCM followed by SVM. (b) (c) (a) (b) (c)

30. Comparison of FCM+K-means with FCM+SVM

31. GUI IMPLEMENTATION FOR ROAD Extraction: example 1

32. Example 2

33. Example 3

34. Executable file contains DOS window Road.exe: Classified the functions two categories: One is for manual control methods and the other is for machine learning methods Meanwhile, we can also conceal DOS window

36. Using FCM clustering to separate road-like cluster and the other clusters increases the SVM classification accuracy.

37. We used MRF regularization to remove speckle-like noise then we could extract the fine road net.

39. References [1] Stefan Hinz, “Automatic extraction of urban road networks from multi-view aerial imagery”, Technische University 2003 [2] VladmirVapnik, ”Statistical Learning Theory”, JOHN WILELY & SONS, Inc.1998 [3] Curt H.Davis, “An integrated system for automatic road mapping from high-resolution multi-spectral satellite imagery by information fusion”, Elsevier Inc. 2004 [4] Yairmoshe, “GUI with Matlab” Department of Electronic Engineering, Columbia University , May 2004. [5] http://maps.google.com/ [6] Yang Li, “A new validity function for fuzzy clustering”, School of mathematical sciences, Beijing normal university, 2005 [6] Xu Yong and Shaoguang Zhou, “Markov random field for road extraction applications in remote sensing images”, Department of Surveying and Mapping Engineering, Hohai University, 2008 [7] David M.McKeown ,“Performance evaluation for automatic feature extraction ”, Computer Science Department, Carnegie Mellon University 2000 [8] Patrick Perez, “Markov random fields and images”, Campus Beauileu, 1999 [9] Drs. Trani and Rahka, “MATLAB Graphic user interfaces(GUI) computer applications in civil engineering ”, Spring, 2000

40. Other fields From 2009.01 to 2009.12 OpenCV1.0+VC6.0 OpenGL+VC6.0 OpenCV2.0+VS2008 Java3d on Myeclipse 7.5

41. OpenCV1.0+VC6.0 Haarcascade Cascade: Stage1: Classifier11: Feature11 Classifier12: Feature12 ... Stage2: Classifier21: Feature21 CvHaarFeature, CvHaarClassifier, CvHaarStageClassifier, CvHaarClassifierCascade Boosted Haar------Tree structure Here, we import cvLoadHaarClassifierCascade

42. OpenGL+VC6.0 3D effect by using Anaglyph glasses

43. OpenCV2.0+Visual studio2008 Configuration(CMake setup…) Debug/Release Rebuild Options -> Projects and Solutions -> VC++ Directories Car plate recognition

44. Soft matting Function: Image enhancement Boxfilter.m: Equivalent to the function: colfilt(imSrc, [2*r+1, 2*r+1], 'sliding', @sum) - But much faster Guidefilter.m % - guidance image: I % - filtering input image: p (should be a gray-scale/single channel image) % - local window radius: r % - regularization parameter: eps

45. THE END Q&A