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Segmentation and classification of man-made maritime objects in TerraSAR-X images

Segmentation and classification of man-made maritime objects in TerraSAR-X images IEEE International Geoscience and Remote Sensing Symposium Vancouver, Canada July 27 th 2011 Michael Teutsch , email: michael.teutsch@iosb.fraunhofer.de Günter Saur, email: guenter.saur@iosb.fraunhofer.de.

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Segmentation and classification of man-made maritime objects in TerraSAR-X images

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  1. Segmentation and classification of man-made maritime objects in TerraSAR-X images IEEE International Geoscience and Remote Sensing Symposium Vancouver, Canada July 27th 2011 Michael Teutsch, email: michael.teutsch@iosb.fraunhofer.de Günter Saur, email: guenter.saur@iosb.fraunhofer.de

  2. Outline • Motivation • Concept • Segmentation • Classification • Examples • Conclusions and future work

  3. Motivation I • Applications: • Tracking of cargo ship traffic • Surveillance of fishery zones, harbours, shipping lanes • Detection of abnormal ship behaviour, criminal activities • Search for lost containers or hijacked ships Aims / Challenges: • Detectionof man-madeobjects (not here) • Preciseorientationandsizeestimation • Separation ofclutter, non-ships, different shiptypes • Robustnessagainstvarious SAR-specificnoiseeffects • Fast processing time • Here: Analyzeobjectappearance, avoidmodelsandpriorknowledge

  4. Motivation II: Difficult examples

  5. Concept

  6. Pre-processing • 3x3 median filter • Ground Sampling Distance (GSD) normalization to 2.0 meters/pixel

  7. Segmentation I: Structure-emphasizing LBP filter Local Binary Pattern: Rotation invariant uniform LBPs: Texture primitives: 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.

  8. Segmentation II: Structure-emphasizing LBP filter Rotation invariant uniform LBPs (texture primitives): Rotation invariant variance measure: 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 • 17 different TerraSAR-X StripMap images • 756 manually labeled detections including orientation and length • No ground truth, manual labeling is sensed truth • Labeling inspired by CFAR-detection including potential clutter • Scale normalization to 2.0 meters / pixel

  13. Segmentation VII: Orientation and size estimation results

  14. Segmentation VIII: Examples

  15. Classification I: Classes non-ship ship structure 1 clutter (ambiguity) unstructured ship clutter ship structure 2

  16. Classification II: Concept • G. Saur, M. Teutsch, „SAR signature analysis for TerraSAR-X based ship monitoring“, Proceedings of SPIE Vol. 7830, 2010. • 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.

  17. Classification III: Experiments and results • 5 classes: clutter, non-ship, unstr. ship, structure 1, structure 2 • 543 samples with good segmentation and possible manual labeling: • 53 clutter, 110 non-ship, 322 unstr. ship, 17 structure 1, 41 stucture 2 • 362 training samples and 181 test samples • Runtime for segmentation and classification: ~ 2 sec per detection • Classification results:

  18. Classification IV: Examples clutter unstructured ship unstructured ship unstructured ship non-ship ship structure 1

  19. Classification V: Examples for whole processing chain ship structure 2 ship structure 2 unstructured ship

  20. Conclusions • Aim: Segmentation and classification of man-made objects in satellite SAR • Challenge: Robustness against various object appearances, noise effects • 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° • Classification: Extensive feature calculation, feature evaluation and selection, classification with cascaded SVM and 3-NN, 81% correct classification Future work • Improvesizeestimation (LBPs insteadofrow/columnhistograms?) • More dataforclassification (esp. structureclasses) • Other approachesfor 3rd classification-stage (localfeatures?) • Is objectstructurednessandclassifiabilitybased on appearancemeasurable?

  21. Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB Thanks a lotforyourattention! Karlsruhe Ettlingen Ilmenau

  22. Segmentation: Orientation estimation error distrib.

  23. Segmentation: Examples – The bad guys

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