A Number-of-Classes-Adaptive Unsupervised Classification Framework for SAR Images<br />Bin Liu, Hao Hu, Kaizhi Wang, Xingz...
Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimat...
Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimat...
Problem Description<br />Radar as the third eye – Synthetic Aperture Radar (SAR)<br />A day-or-night, all-weather means of...
Problem Description & Introduction<br />SAR image classification<br />Fundamental to exploiting the enormous amounts of SA...
Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimat...
Feature Extraction & Image Representation<br />SAR image partitioned into NP patches, m * m<br />Feature Extraction<br />G...
Feature Extraction & Image Representation<br />Image Representation<br />The N * M SAR image including NPpatches <br />   ...
Feature Extraction & Image Representation<br />Image Representation<br />July, 2011<br />IEEE IGARSS 2011<br />9<br />Free...
Estimation of the Number of Classes<br />Reordering<br />Cattell: reorder the objects suitably image better able to highl...
Estimation of the Number of Classes<br />Reordering<br />July, 2011<br />IEEE IGARSS 2011<br />11<br />
Estimation of the Number of Classes<br />Extraction<br />The RDI can highlight the potential classes as a set of dark bloc...
Estimation of the Number of Classes<br />Extraction<br />July, 2011<br />IEEE IGARSS 2011<br />13<br />
Estimation of the Number of Classes<br />Inversion<br />In the projection signal, a peak between two neighboring valleys r...
Estimation of the Number of Classes<br />Inversion<br />July, 2011<br />IEEE IGARSS 2011<br />15<br />
Estimation of the Number of Classes<br />Estimate the number of classes and get initial classes<br />July, 2011<br />IEEE ...
Final Classification<br />After the estimation operation, several important initial class parameters<br />The number of cl...
Incorporation of Spatial Relations between Patches<br />Simple yet effective <br />The SAR image is partitioned into patch...
Implementation Procedures<br />Preprocessing<br />The single look SAR data are multilook-processed<br />Patch Generation<b...
Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimat...
Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />21<br />
Experiments and Results<br />Performance of the proposed method<br />Determine the number of classes = 3, √<br />The total...
Experiments and Results<br />Performance of the proposed method<br />Built-up areas misclassified as vegetated areas – 9.1...
Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />24<br />
Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />25<br />Performance of the proposed method<br />Determi...
Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />26<br />
Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />27<br />Performance of the proposed method<br />Determi...
Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimat...
Conclusions and Future Work<br />An NoCA Unsupervised Classification framework for SAR images<br />Extract the numbers of ...
Thank you for your attention !<br />July, 2011<br />IEEE IGARSS 2011<br />30<br />
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3680-NoCA.pptx

  1. 1. A Number-of-Classes-Adaptive Unsupervised Classification Framework for SAR Images<br />Bin Liu, Hao Hu, Kaizhi Wang, Xingzhao Liu, and Wenxian Yu<br />Remote Sensing Technology Institute<br />Shanghai Jiao Tong University<br />
  2. 2. Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimation of the Number of Classes<br />Final Classification<br />Incorporation of Spatial Relations between Patches<br />Implementation<br />Experiments & Results<br />Conclusions & Future Work<br />July, 2011<br />IEEE IGARSS 2011<br />2<br />
  3. 3. Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimation of the Number of Classes<br />Final Classification<br />Incorporation of Spatial Relations between Patches<br />Implementation<br />Experiments & Results<br />Conclusions & Future Work<br />July, 2011<br />IEEE IGARSS 2011<br />3<br />
  4. 4. Problem Description<br />Radar as the third eye – Synthetic Aperture Radar (SAR)<br />A day-or-night, all-weather means of remote sensing<br />High resolution images and useful information about the earth<br />Several spaceborne platforms continuously deliver enormous amounts of SAR data<br />TerraSAR-X, Germany<br />RADARSAT-2, Canada<br />COSMO-SkyMed, Italy<br />ALOS-PALSAR, Japan<br />… <br />Develop automatic/semi-automatic systems for SAR image interpretation and target recognition<br />July, 2011<br />IEEE IGARSS 2011<br />4<br />
  5. 5. Problem Description & Introduction<br />SAR image classification<br />Fundamental to exploiting the enormous amounts of SAR data<br />Akey requirement in both military and civil sectors<br />Ahighly desired goal for developing intelligent databases <br />Anecessary process for target detection and recognition<br />Develop an automatic system to divide the SAR images into basic land covers: Water, built-up areas, vegetated areas, …<br />An important problem: The number of classes in the image is generally UNKNOWN<br />A Number-of-Classes-Adaptive (NoCA) Unsupervised Classification Framework for SAR Images<br />July, 2011<br />IEEE IGARSS 2011<br />5<br />
  6. 6. Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimation of the Number of Classes<br />Final Classification<br />Incorporation of Spatial Relations between Patches<br />Implementation<br />Experiments & Results<br />Conclusions & Future Work<br />July, 2011<br />IEEE IGARSS 2011<br />6<br />
  7. 7. Feature Extraction & Image Representation<br />SAR image partitioned into NP patches, m * m<br />Feature Extraction<br />Grey Histogram: calculated with BGHbins<br />Texture Histogram: Inspired by Reigber et al., using the filter coefficient of the Lee speckle filter to describe the texture inhomogeneity. calculated with BTHbins<br />x*- local mean<br />var(x) - local variance<br />σn2is equal to 1 over the number of looks<br />July, 2011<br />IEEE IGARSS 2011<br />7<br />A. Reigber, M. Jäger, W. He, L. Ferro-Famil, and O. Hellwich, “Detection and classification of urban structures based on high-resolution SAR imagery,” in Proc. Urban Remote Sens. Joint Event, Paris, France, 2007, pp. 1–6.<br />
  8. 8. Feature Extraction & Image Representation<br />Image Representation<br />The N * M SAR image including NPpatches <br />  An NP * NP dissimilarity image<br />fGH(∙) and fTH(∙) denote grey and texture histograms, respectively<br />Dis(∙) is the city block distance<br />αis the fusion factor<br />July, 2011<br />IEEE IGARSS 2011<br />8<br />
  9. 9. Feature Extraction & Image Representation<br />Image Representation<br />July, 2011<br />IEEE IGARSS 2011<br />9<br />Free TerraSAR-X Data Samples. [Online]. Available: http://www.infoterra.de/free-sample-data<br />
  10. 10. Estimation of the Number of Classes<br />Reordering<br />Cattell: reorder the objects suitably image better able to highlight the potential class structure in the data<br />Different methods of implementing visual representation of pairwise dissimilarity information – the Reordered Dissimilarity Image (RDI)<br />Using the Visual Assessment of cluster Tendency (VAT) algorithm to transform the dissimilarity image into the RDI<br />July, 2011<br />IEEE IGARSS 2011<br />10<br />J. C. Bezdek and R. Hathaway, “VAT: a tool for visual assessment of (cluster) tendency,” in Proc. Int’l Joint Conf. Neural Networks (IJCNN ’02), Honolulu, HI, May 2002, pp. 2225–2230<br />
  11. 11. Estimation of the Number of Classes<br />Reordering<br />July, 2011<br />IEEE IGARSS 2011<br />11<br />
  12. 12. Estimation of the Number of Classes<br />Extraction<br />The RDI can highlight the potential classes as a set of dark blocks along the diagonal of the image<br />The Dark Block Extraction (DBE) method to automatically extract dark blocks. Using several common image and signal processing techniques<br />Perform image segmentation on the RDI to obtain a binary image, and then apply the directional morphological filters to the binary image<br />Apply a distance transform to the filtered binary image, and then project the pixel values along the main diagonal axis of the image to form a projection signal<br />Smooth the projection signal, and use the first-order derivative of the projection signal to detect the major peaks and valleys of the projection signal<br />July, 2011<br />IEEE IGARSS 2011<br />12<br />L. Wang, C. Leckie, K. Ramamohanarao, and J. Bezdek, “Automatically determining the number of clusters in unlabeled data sets,” IEEE Trans. Knowledge and Data Eng., vol. 21, no. 3, pp. 335–350, Mar. 2009.<br />
  13. 13. Estimation of the Number of Classes<br />Extraction<br />July, 2011<br />IEEE IGARSS 2011<br />13<br />
  14. 14. Estimation of the Number of Classes<br />Inversion<br />In the projection signal, a peak between two neighboring valleys realistically represents a class in the data<br />Suppose that there are Nx patches between the (x–1)th and the xth valleys<br />Due to noise, the inversion step cannot simply determine that all the Nx patches belong to the xth class. In our method, the inversion step labels the β∙Nx patches nearest to the xth peak as elements of the xth class, where βis from 0 to 1<br />The other (1–β)∙Nx patches between the (x–1)th and the xth valleys are categorized as “undecided”, and their labels are determined in the final classification<br />July, 2011<br />IEEE IGARSS 2011<br />14<br />
  15. 15. Estimation of the Number of Classes<br />Inversion<br />July, 2011<br />IEEE IGARSS 2011<br />15<br />
  16. 16. Estimation of the Number of Classes<br />Estimate the number of classes and get initial classes<br />July, 2011<br />IEEE IGARSS 2011<br />16<br />
  17. 17. Final Classification<br />After the estimation operation, several important initial class parameters<br />The number of classes X<br />β∙NP patches with class labels, belong to initial classes C1, C2, …, CX<br />Final classification<br />The commonly used technique Support Vector Machine (SVM) classifier is used<br />LIBSVM<br />Training data set – β∙NP patches with labels<br />Features – Grey and texture histograms<br />July, 2011<br />IEEE IGARSS 2011<br />17<br />
  18. 18. Incorporation of Spatial Relations between Patches<br />Simple yet effective <br />The SAR image is partitioned into patches with overlaps<br />Some parts of a patch may belong to many adjacent patches, and after the final classification, they might be assigned to different classes<br />Majority vote<br />July, 2011<br />IEEE IGARSS 2011<br />18<br />M. Liénou, H. Maître, and M. Datcu, “Semantic annotation of satellite images using latent Dirichlet allocation,” IEEE Geosci. Remote Sens. Lett., vol. 7, no. 1, pp. 78–82, Jan. 2010.<br />
  19. 19. Implementation Procedures<br />Preprocessing<br />The single look SAR data are multilook-processed<br />Patch Generation<br />The SAR image is partitioned into image patches of m* m pixels with NOL pixels overlapping<br />Preclustering Estimation<br />The SAR image is represented by a dissimilarity image, which is reordered into a RDI image. Then, the DBE process extracts the number of classes, each class center, and initial class labels of some patches<br />Final Classification<br />Class labels of the “undecided” patches from the preclustering estimation operation are estimated and refined<br />Final Decision<br />A majority vote is used to ascribe common parts of overlapping patches to the likeliest class. Then, the class label of every pixel in the image is finally decided<br />July, 2011<br />IEEE IGARSS 2011<br />19<br />
  20. 20. Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimation of the Number of Classes<br />Final Classification<br />Incorporation of Spatial Relations between Patches<br />Implementation<br />Experiments & Results<br />Conclusions & Future Work<br />July, 2011<br />IEEE IGARSS 2011<br />20<br />
  21. 21. Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />21<br />
  22. 22. Experiments and Results<br />Performance of the proposed method<br />Determine the number of classes = 3, √<br />The total accuracy is 92.97%<br />July, 2011<br />IEEE IGARSS 2011<br />22<br />The SAR image<br />The ground truth map<br />The final classification map<br />
  23. 23. Experiments and Results<br />Performance of the proposed method<br />Built-up areas misclassified as vegetated areas – 9.17%<br />Vegetated areas misclassified as built-up areas – 6.57% <br />It seems that built-up and vegetated areas are likely to be confused with each other. The classification result may be further refined by introducing more features and prior knowledge of the scene<br />July, 2011<br />IEEE IGARSS 2011<br />23<br />
  24. 24. Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />24<br />
  25. 25. Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />25<br />Performance of the proposed method<br />Determine the number of classes = 3, √<br />The total accuracy is 95.14%<br />The SAR image<br />The ground truth map<br />The final classification map<br />
  26. 26. Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />26<br />
  27. 27. Experiments and Results<br />July, 2011<br />IEEE IGARSS 2011<br />27<br />Performance of the proposed method<br />Determine the number of classes = 4, √<br />The total accuracy is 89.90%<br />The SAR image<br />The ground truth map<br />The final classification map<br />
  28. 28. Content<br />Problem Description & Introduction<br />Framework<br />Feature Extraction & Image Representation<br />Estimation of the Number of Classes<br />Final Classification<br />Incorporation of Spatial Relations between Patches<br />Implementation<br />Experiments & Results<br />Conclusions & Future Work<br />July, 2011<br />IEEE IGARSS 2011<br />28<br />
  29. 29. Conclusions and Future Work<br />An NoCA Unsupervised Classification framework for SAR images<br />Extract the numbers of classes  estimate each class center more accurately  provide robust classification under various numbers of classes<br />Based on image patches & incorporate relations between patches  effective and efficient<br />Pairwise-dissimilarity-based estimation operation, flexible  multiple features can be selected and fused into the estimation operation<br />Future Work<br />Integration of statistical information<br />Extract the number of classes and estimate each class center in randomly selected sub-scenes, and then apply the parameters to the whole image<br />In the future, the framework needs to be applied on an enormous SAR image database to develop a fully operational procedure <br />July, 2011<br />IEEE IGARSS 2011<br />29<br />
  30. 30. Thank you for your attention !<br />July, 2011<br />IEEE IGARSS 2011<br />30<br />
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