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SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt
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SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt

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  • 1. <ul><li>Segmentation and classification of man-made maritime objects in TerraSAR-X images </li></ul><ul><li>IEEE International Geoscience and Remote Sensing Symposium Vancouver, Canada </li></ul><ul><li>July 27 th 2011 </li></ul><ul><li>Michael Teutsch , email: [email_address] </li></ul><ul><li>Günter Saur, email: [email_address] </li></ul>
  • 2. Outline <ul><li>Motivation </li></ul><ul><li>Concept </li></ul><ul><li>Segmentation </li></ul><ul><li>Classification </li></ul><ul><li>Examples </li></ul><ul><li>Conclusions and future work </li></ul>
  • 3. Motivation I <ul><li>Applications: </li></ul><ul><li>Tracking of cargo ship traffic </li></ul><ul><li>Surveillance of fishery zones, harbours, shipping lanes </li></ul><ul><li>Detection of abnormal ship behaviour, criminal activities </li></ul><ul><li>Search for lost containers or hijacked ships </li></ul><ul><li>Aims / Challenges: </li></ul><ul><li>Detection of man-made objects (not here) </li></ul><ul><li>Precise orientation and size estimation </li></ul><ul><li>Separation of clutter, non-ships, different ship types </li></ul><ul><li>Robustness against various SAR-specific noise effects </li></ul><ul><li>Fast processing time </li></ul><ul><li>Here: Analyze object appearance, avoid models and prior knowledge </li></ul>
  • 4. Motivation II: Difficult examples
  • 5. Concept
  • 6. Pre-processing <ul><li>3x3 median filter </li></ul><ul><li>Ground Sampling Distance (GSD) normalization to 2.0 meters/pixel </li></ul>
  • 7. Segmentation I: Structure-emphasizing LBP filter Timo Ojala et al., „ Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 24, no. 7, July 2002. Rotation invariant uniform LBPs: Texture primitives: Local Binary Pattern:
  • 8. Segmentation II: Structure-emphasizing LBP filter Rotation invariant variance measure: Rotation invariant uniform LBPs (texture primitives): For each pixel position (x,y) , fixed P , and varying R :
  • 9. Segmentation III: Rotation compensation with HOG A. Korn, „Toward a Symbolic Representation of Intensity Changes in Images“, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 10, no. 5, 1988.
  • 10. Segmentation IV: Rotation compensation with HOG+PCA PCA FUSION
  • 11. Segmentation V: Size estimation with row/col. histograms
  • 12. Segmentation VI: Experimental data set <ul><li>17 different TerraSAR-X StripMap images </li></ul><ul><li>756 manually labeled detections including orientation and length </li></ul><ul><li>No ground truth, manual labeling is sensed truth </li></ul><ul><li>Labeling inspired by CFAR-detection including potential clutter </li></ul><ul><li>Scale normalization to 2.0 meters / pixel </li></ul>
  • 13. Segmentation VII: Orientation and size estimation results method rotation estimation error median mean LBP &amp; HOG &amp; PCA with median filter 5.24° 11.65° LBP &amp; HOG &amp; PCA without median filter 5.99° 12.16° LBP &amp; HOG 6.71° 12.99° LBP &amp; PCA 12.09° 24.38° HOG only 10.68° 23.36°
  • 14. Segmentation VIII: Examples
  • 15. Classification I: Classes clutter (ambiguity) unstructured ship clutter ship structure 2 ship structure 1 non-ship
  • 16. Classification II: Concept <ul><li>G. Saur, M. Teutsch, „SAR signature analysis for TerraSAR-X based ship monitoring“, Proceedings of SPIE Vol. 7830 , 2010. </li></ul><ul><li>M. Teutsch, W. Krüger, „Classification of small Boats in Infrared Images for maritime Surveillance“, 2nd International Conference on WaterSide Security (WSS) , Marina di Carrara, Italy, Nov. 3-5, 2010. </li></ul>
  • 17. Classification III: Experiments and results <ul><li>5 classes: clutter, non-ship, unstr. ship, structure 1, structure 2 </li></ul><ul><li>543 samples with good segmentation and possible manual labeling: </li></ul><ul><ul><ul><li>53 clutter, 110 non-ship, 322 unstr. ship, 17 structure 1, 41 stucture 2 </li></ul></ul></ul><ul><li>362 training samples and 181 test samples </li></ul><ul><li>Runtime for segmentation and classification: ~ 2 sec per detection </li></ul><ul><li>Classification results: </li></ul>classifier SVM 1 SVM 2 3-NN cascade correct rate 96.68 % 93.29 % 91.45 % 80.66 %
  • 18. Classification IV: Examples non-ship unstructured ship unstructured ship unstructured ship clutter ship structure 1
  • 19. Classification V: Examples for whole processing chain ship structure 2 ship structure 2 unstructured ship
  • 20. Conclusions <ul><li>Aim: Segmentation and classification of man-made objects in satellite SAR </li></ul><ul><li>Challenge: Robustness against various object appearances, noise effects </li></ul><ul><li>Segmentation: Pre-processing, structure-emphasizing filter with LBPs, orientation estimation with HOGs and PCA, size estimation with row/column histograms, median orientation estimation error: 5.2° </li></ul><ul><li>Classification: Extensive feature calculation, feature evaluation and selection, classification with cascaded SVM and 3-NN, 81% correct classification </li></ul>Future work <ul><li>Improve size estimation (LBPs instead of row/column histograms?) </li></ul><ul><li>More data for classification (esp. structure classes) </li></ul><ul><li>Other approaches for 3rd classification-stage (local features?) </li></ul><ul><li>Is object structuredness and classifiability based on appearance measurable? </li></ul>
  • 21. Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB Karlsruhe Ettlingen Ilmenau Thanks a lot for your attention!
  • 22. Segmentation: Orientation estimation error distrib.
  • 23. Segmentation: Examples – The bad guys

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