Using eCognition to improve feature recognition.
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Using eCognition to improve feature recognition. Results of MSc research on automated pattern recognition in archaeology.
Iris Kramer31 march 2016
CAA OSLOComputer vision vs human perception in
remote sensing image analysis: time to move on
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Brief biography of thoughtsCourses⚲ Geosciences
⚲ LiDAR and satellite images⚲ Automation with e.g. eCognition
⚲ Dissertation on automated pattern recognition⚲ Supervisor: “Are you sure?!”
⚲ “An archaeological reaction to the remote sensing data explosion. Reviewing the research on semi-automated pattern recognition and assessing the potential to integrate artificial intelligence”.
CAA 2016
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OverviewCognitive computing in Geomorphology
Computing to imitate archaeologists
Case study: barrow detection using eCognition 1. Object based rule set 2. Classification by training 3. Adaptive template matching
Discussion and future scope
ConclusionCAA 2016
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Cognitive computing in
Geomorphology. CAA 2016
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Inspiration: landslide detectionTrace how features differ from surroundings
(left) LiDAR derived DEM.(right) Concept of a typical landslide in the soil covered and hilly study area, Belgium
CAA 2016 after van den Eeckhaut et al., 2012, 212
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Inspiration: landslide detectionClassify true and false positives
CAA 2016 after van den Eeckhaut et al., 2012, 212
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Foundation of eCognition‘‘Why are we so focused on the statistical analysis of single pixels, rather than on the spatial patterns they create?’’ - Blaschke and Strobl (2001)
Segmentation⚲ Hierarchical grouping of smaller segments ⚲ Based on decision rules
Integrated approach⚲ Multiple remote sensing sources⚲ Native raster/vector handling
CAA 2016
(left) original image, (middle) fusion of intensity and texture gradient images, (right) segmentation results
Niemeyer et al., 2008
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Computing to imitate
archaeologists. CAA 2016
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Archaeological discovery: incomplete data
Key concepts for reconstructing stories - Barceló (2008)
Deduction (argumentation)
Induction (learned from examples)
Analogy (information recalled from previous case studies)
CAA 2016
Niemeyer et al., 2008
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Human argument: cognitive computing
Bronze age barrow cemetery, Oxfordshire- by Hamish Fenton 2003
CAA 2016
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shapes and
sizes
Human argument: cognitive computing
CAA 2016
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texture and
spectrum
Human argument: cognitive computing
CAA 2016
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context and
association
Human argument: cognitive computing
CAA 2016
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context and
association
Human argument: cognitive computing
CAA 2016
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Case study: barrow detection using eCognition.
CAA 2016
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Avebury, Wiltshire
CAA 2016
⚲ LiDAR data ⚲ Environment Agency
⚲ Known barrows⚲ Historic England
⚲ Test areas⚲ ‘barrow cemeteries’
Slope derivativeThree barrow types; (left) Bell (middle) Saucer (right) Bowl
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1. Object based rule set
CAA 2016
Image segmentation into objects with range of brightness (intensity of the slope)
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1. Object based rule set
CAA 2016
Image segmentation into objects with length/width ratio
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1. Object based rule set
CAA 2016
Results
OTHER SAUCER BELL BOWLTRUE POSITIVE 10 3 14 23FALSE NEGATIVE 76 7 6 74PERCENTAGE P/N 12% 30% 70% 24%
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2. Classification by training
CAA 2016
First try out
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3. Adaptive template matching
CAA 2016
Templates created from mean of training locations
Barrow classification based on correspondence threshold
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Discussion and future scope.
CAA 2016
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Potential of object based image analysis
Best practise⚲ Use multiple remote sensing sources⚲ Optimise rule set ⚲ Sharing rule sets⚲ Discussion platform
With eCognition?⚲ Black box?⚲ Expensive!⚲ Open source alternative – ORFEO
⚲ Not a black box⚲ Plugin for QGIS
CAA 2016
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Potential artificial intelligenceDifferent way of understanding images and thus modelling
⚲ Wide range of possibilities ⚲ Object based⚲ Pixel based
⚲ Machine learning
CAA 2016
Round barrow
Mound
Round
has shape
is defined by
… (varied sizes)
has size
Ditchpossibly surrounded by Bankpossibly
surrounded by
Flora
Agriculturepossibly(partly)levelled
Fauna
possibly (partly)
destroyed
has landcover
Barrow Earthworkis type of is type of
is type of
Semantic description of a round barrow
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Thank you.
CAA 2016
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BibliographyBarceló, 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
CAA 2016