Using eCognition to improve feature recognition.


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Presentation of MSc research at the CAA 2016 - Oslo. Session 20 "Computer vision vs human perception in remote sensing image analysis: Time to move on".

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  • Beginning 20th century - Remote sensing (raster) vs. GIS (predominantly vector)
    Hierarchical grouping of smaller segmented image elements based on decision rules

    The data used in their research consisted of scanned aerial photography with a 0.25 m ground sample distance of Southampton, England, obtained from the Ordnance Survey of Great Britain (figure 20, left). Most boundaries in remotely sensed images consist of both an intensity and texture boundary. In this case the gradient of each of these images were fused into one image (figure 20, middle) which enabled the segmentation of meaningful objects (figure 20, right). The achieved results were very accurate and outperformed the generally applied Canny gradient operator.

  • The main problems in rule definition within the archaeological record is that it is almost always incomplete: not all past material things have remained until today or are found destructed. So, in order to replicate an archaeologist it must be able to reconstruct incomplete data for which Barceló— (2008, 49) identified the following key concepts.
  • Compound features
    Geomorphic fingerprint
    Separated by principles of vision
  • Bronze age barrow cemetery, Oxfordshire
    by Hamish Fenton 2003
  • Bronze age barrow cemetery, Oxfordshire
    by Hamish Fenton 2003
  • Bronze age barrow cemetery, Oxfordshire
    by Hamish Fenton 2003
  • Bronze age barrow cemetery, Oxfordshire
    by Hamish Fenton 2003
  • Avebury is a Neolithic henge monument which contains the largest stone circle in Europe.
  • Discussion platform of eCognition is good, like Stackoverflow
  • Short term: object based. With real investments we can move towards machine learning and pixel based: good database of features.
  • Using eCognition to improve feature recognition.

    1. 1. Using eCognition to improve feature recognition.
    2. 2. Cognitive computing in Geomorphology.
    3. 3. Computing to imitate archaeologists.
    4. 4. Case study: barrow detection using eCognition.
    5. 5. Discussion and future scope.
    6. 6. Roundbarrow Mound Round has shape is defined by … (varied sizes) has size Ditch possibly surrounded by Bank possibly surrounded by Flora Agriculture possibly (partly) levelled Fauna possibly (partly) destroyed has landcover Barrow Earthworkis type of is type of is type of
    7. 7. Thank you.
    8. 8. Barceló, J. A. 2008. Computational Intelligence in Archaeology, Hershey, New York, IGI. Blaschke, T., and Strobl, J. 2001. What's wrong with pixels? Some recent developments interfacing remote sensing and GIS. Geo-Informations-Systeme, 14, 12-17. van den Eeckhaut, M., Kerle, N., Poesen, J., and Herv‡s, J. 2012. Identification of vegetated landslides using only a Lidar-based terrain model and derivatives in an object-oriented environment. Proceedings of the 4th GEOBIA, 211. Niemeyer, I., Marpu, P. R., and Nussbaum, S. 2008. Change detection using object features. In: Blaschke, T., Lang, S., and Hay, G. J. (eds.) Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Verlag: Springer. TRIMBLE eCognition Developer 9.1