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Flanders, Belgium QUANTITATIVE SEGMENTATION EVALUATION FOR LARGE SCALE

Gautama Laboratory of Forest Management and Spatial Information Techniques,

Telecommunications and Information Processing, Faculty of Engineering, Ghent

Layout, exchange and management of large geographic information Terrain

1 Flanders, Belgium QUANTITATIVE SEGMENTATION EVALUATION FOR LARGE SCALE MAPPING PURPOSES Frieke Van Coillie, Rui Pires, Nancy Van Camp, Sidharta Gautama Laboratory of Forest Management and Spatial Information Techniques, Faculty of Bioscience Engineering, Ghent University, Belgium Department of Telecommunications and Information Processing, Faculty of Engineering, Ghent University, Belgium Flemish Geographical InformationAgency, Gent, Belgium Layout, exchange and management of large geographic information Terrain elements relevant for use within limits 1:2 to 1:12 Buildings Parcels Watercourses Roads AGIV, 2 1

2 Initially produced by combination of photogrammetric and terrestrial techniques (accuracies at the cm level) However, nothing stays as it is Flanders is a bustling region Flemish aspiration to home ownership Allotments, city renovation projects GRB dates fast and consequently becomes antiquated Oational value of GRB is determined by its degree of topicality GRB must be kept up to date Reporting by municipalities Comparison of cadastral maps and current GRB Terrestrial measurements Production and maintenance processes Pricey Highly accurate Labour intensive Time-consuming Carry on production process as is Facilitation and automation of GRB maintenance process Image Segmentation (IS) VHR digital images High accuracy standards Research objective IS alternative and facilitation to current GRB maintenance More specific: how selection of IS parameter settings can be facilitated Aim Develop methodology for selecting optimal parameter settings, based on specific quality measures Segmentation results with high accuracy Eximents Data DMC orthophoto ( Hansa Luftbild) March 2 GSD of 77mm City of Ghent, Belgium One specific entity: a house Manually digitized reference image Ground truth that semantically comes closest to the image content 2

Eximents Eximents Segmentation quality? AGIV, 2 Eximents # segmentations (Develo 7.) Limited set of parameter combinations Scale: 3-13 (2) Shape: -.

9 (constant and ) Resultant segments used in calculating eight different and straightforward quality measures (QM) Eximents 1. # segments with in reference polygon 2.

Average ance between edge pixel and nearest edge pixel of reference polygon (pixel coordinates).

3 Eximents Eximents Segmentation quality? AGIV, 2 Eximents # segmentations (Develo 7.) Limited set of parameter combinations Scale: 3-13 (2) Shape: -.9 (constant and compactness) Compactness: -.9 (constant and ) Resultant segments used in calculating eight different and straightforward quality measures (QM) Eximents 1. # segments with in reference polygon 2. Difference in total 3. Difference in total imeter 4. Difference in exity (SC=P/4*sqrt(A)). Average ance between edge pixel and nearest edge pixel of reference polygon (pixel coordinates). Three cumulative ance histogram values Eximents Cumulative ance histogram Group segments with in reference Calculate for each boundary pixel of the grouped segments the ance to the nearest ground truth boundary Calculate cumulative histogram Ideal function is step edge, i.e. grouped segment boundary within 1-pixel ance of ground truth Eximents fraction Cumulative ance histogram ance (pixels).3, compactness.7, 3

4 Eximents Eximents Cumulative ance histogram fraction compactness.7.3 compactness ance (pixels) Eximents To evaluate impact of parameter settings on segmentation accuracy compared to reference polygon Interpretation of error rate individual QM Combination of eight QMs in a normalized weighted QM ( ) optimal weight Shape=. Compactness=.7 Varying Shape=. Compactness=.7 Varying Reference Reference

5 Shape=. Compactness=.7 Varying Shape=. Compactness=.7 Varying Reference Reference Scale=3 Compactness=1. Scale=3 Compactness=1. Reference.3.9 Reference Scale=3 Compactness=1. Scale=3 Compactness=1. Reference.3.9 Reference.3.9

6 Conclusion Methodology based on simple measures of matching Easily managed Effortlessly adjusted to the user s requirements Overall evaluation measure Normalized weighted combination of QMs Each indicating % aberration from the optimal (i.e. the ground truth) Conclusion Insight on how parameter settings influence # resultant segments match the contour, and of the reference selection of best parameter set Weights of individual QMs can be chosen according to the user s evaluation Define to what extent the selected segmentation result is accurate compared to the reference Conclusion Proposed method is initial impetus Further development and fine tuning Application of GRB as gold standard The method searches objectively for suitable parameters settings facilitates incorporation of image segmentation into oational workflow of GRB update process in Flanders, Belgium Thanks to all of you for your attention the OC for this pleasant conference frieke.vancoillie@ugent.be rlpires@telin.ugent.be nancy.vancamp@agiv.be sidharta.gautama@telin.ugent.be

QUANTITATIVE SEGMENTATION EVALUATION FOR LARGE SCALE MAPPING PURPOSES

QUANTITATIVE SEGMENTATION EVALUATION FOR LARGE SCALE MAPPING PURPOSES F. M. B. Van Coillie a, *, R. L. V. P. M. Pires b, N. A. F. Van Camp c, S. Gautama b a Laboratory of Forest Management and Spatial