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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 SegmentationAndClassificationOfMaritimeMan-MadeObjectsInTerraSAR-X-Images_TeutschSaur_IGARSS2011.ppt Presentation Transcript

      • 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: [email_address]
      • Günter Saur, email: [email_address]
    • Outline
      • Motivation
      • Concept
      • Segmentation
      • Classification
      • Examples
      • Conclusions and future work
    • 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:
      • Detection of man-made objects (not here)
      • Precise orientation and size estimation
      • Separation of clutter, non-ships, different ship types
      • Robustness against various SAR-specific noise effects
      • Fast processing time
      • Here: Analyze object appearance, avoid models and prior knowledge
    • Motivation II: Difficult examples
    • Concept
    • Pre-processing
      • 3x3 median filter
      • Ground Sampling Distance (GSD) normalization to 2.0 meters/pixel
    • 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:
    • 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 :
    • 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.
    • Segmentation IV: Rotation compensation with HOG+PCA PCA FUSION
    • Segmentation V: Size estimation with row/col. histograms
    • 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
    • Segmentation VII: Orientation and size estimation results method rotation estimation error median mean LBP & HOG & PCA with median filter 5.24° 11.65° LBP & HOG & PCA without median filter 5.99° 12.16° LBP & HOG 6.71° 12.99° LBP & PCA 12.09° 24.38° HOG only 10.68° 23.36°
    • Segmentation VIII: Examples
    • Classification I: Classes clutter (ambiguity) unstructured ship clutter ship structure 2 ship structure 1 non-ship
    • 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.
    • 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:
      classifier SVM 1 SVM 2 3-NN cascade correct rate 96.68 % 93.29 % 91.45 % 80.66 %
    • Classification IV: Examples non-ship unstructured ship unstructured ship unstructured ship clutter ship structure 1
    • Classification V: Examples for whole processing chain ship structure 2 ship structure 2 unstructured ship
    • 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
      • Improve size estimation (LBPs instead of row/column histograms?)
      • More data for classification (esp. structure classes)
      • Other approaches for 3rd classification-stage (local features?)
      • Is object structuredness and classifiability based on appearance measurable?
    • Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB Karlsruhe Ettlingen Ilmenau Thanks a lot for your attention!
    • Segmentation: Orientation estimation error distrib.
    • Segmentation: Examples – The bad guys