using ecognition to improve feature recognition

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

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”.

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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

Cognitive computing in

Geomorphology. CAA 2016

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

Inspiration: landslide detectionClassify true and false positives

CAA 2016 after van den Eeckhaut et al., 2012, 212

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

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(left) original image, (middle) fusion of intensity and texture gradient images, (right) segmentation results

Niemeyer et al., 2008

Computing to imitate

archaeologists. CAA 2016

Archaeological discovery: incomplete data

Key concepts for reconstructing stories - Barceló (2008)

Deduction (argumentation)

Induction (learned from examples)

Analogy (information recalled from previous case studies)

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Niemeyer et al., 2008

Human argument: cognitive computing

Bronze age barrow cemetery, Oxfordshire- by Hamish Fenton 2003

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shapes and

sizes


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texture and

spectrum


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context and

association


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Case study: barrow detection using eCognition.

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Avebury, Wiltshire

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⚲ LiDAR data ⚲ Environment Agency

⚲ Known barrows⚲ Historic England

⚲ Test areas⚲ ‘barrow cemeteries’

Slope derivativeThree barrow types; (left) Bell (middle) Saucer (right) Bowl

1. Object based rule set

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Image segmentation into objects with range of brightness (intensity of the slope)


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Image segmentation into objects with length/width ratio


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Results

OTHER SAUCER BELL BOWLTRUE POSITIVE 10 3 14 23FALSE NEGATIVE 76 7 6 74PERCENTAGE P/N 12% 30% 70% 24%

2. Classification by training

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First try out

3. Adaptive template matching

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Templates created from mean of training locations

Barrow classification based on correspondence threshold

Discussion and future scope.

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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

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Potential artificial intelligenceDifferent way of understanding images and thus modelling

⚲ Wide range of possibilities ⚲ Object based⚲ Pixel based

⚲ Machine learning

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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

Thank you.

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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

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using ecognition to improve feature recognition

Education