The use of Orfeo Toolbox in the context of map updating Christophe Simler; Royal Military Academy Charles Beumier; Royal Military Academy Christine Leignel; Université Libre de Bruxelles Olivier Debeir; Université Libre de Bruxelles Eléonore Wolff; Université Libre de Bruxelles

1. The use of Orfeo Toolbox in the Context
of Map Updating
Christophe Simler
Royal Military Academy
Brussels, Belgium

2. Main part of the ARMURS project
VHR satellite image (Ikonos or raster of an old vector
Quickbird) or aerial image RGB geographical database
pansharpening
XS pansharpened
multispectral pixel description and
mean shift segmentation
segmented image
region feature extraction and SVM
classification
classified image
(road/building/other)
comparaison
change map (roads and
buildings)
database update

3. Softwares
Freeware
-ORFEO Toolbox Extensible
Handle most image format (use GDAL)
-Development Image processing for remote sensing
-Proprietary code
-Open source code

4. Main part of the ARMURS project
VHR satellite image (Ikonos or raster of an old vector
Quickbird) or aerial image RGB geographical database
pansharpening
XS pansharpened
multispectral pixel description and
mean shift segmentation
segmented image
region feature extraction and SVM
classification
classified image
(road/building/other)
comparaison
change map (roads and
buildings)
database update

5. Main part of the ARMURS project
VHR satellite image (Ikonos or raster of an old vector
Quickbird) or aerial image RGB geographical database
pansharpening:
otb::SimpleRcsPanSharpeningFusionImageFilter
XS pansharpened
multispectral pixel description and mean shift
segmentation: otb::MeanShiftVectorImageFilter
segmented image
region feature extraction and SVM
classification: otb::SVMModel and
otb::SVMClassifier
classified image
(road/building/other)
comparaison
change map (roads and
buildings)
database update

6. Mean shift segmentation results
Part of an Ikonos satellite
image in the region of
Jodoigne (Belgium)
Roads and buildings are
generally precisely
extracted

7. Régions feature extraction
The regions obtained from the segmentation are described by the following
feature vector:
area
eccentricity
mean R
mean G
mean B
mean NIR

8. Image classification
The feature vectors are classified into classes « roads », « building » or « other »
Support Vector Machine (SVM)

9. Training set
Our training set is composed of about 1000 mean shift regions manually assigned
to class « road », « building » or « other »
two componants of our
feature vector:

10. Training
two 2-class SVM with Gaussian kernel are trained independently
road/other building/other
Parameters to tune:
- kernel standard deviation
- penalisation of the misclassifications

11. Training
INPUT : training set
INPUT : 2D grid value for (road/other or building/other)
the 2 parameters to tune
training set test set
new couple of permutation
parameter values
learning
decision boundaries
FN FP
balanced loss= = +
VP + FN VN + FP
(loop)
optimal parameter values
1- optimisation of the two parameters by cross validation learning
2- learning on the whole set
OUTPUT : decision boundaries
3- classifier performance quantification

12. Optimal tuning
Energie to minimise

13. Optimal tuning
Minimisation with coarse-to-fine approach

14. Optimal tuning
Minimisation with coarse-to-fine approach

15. Classification: SVM input image
Part of an aerial RGB
image of a region of
Bruxelles (Belgium)

16. SVM Classification results
Overlap of the two
2-class SVM
classification results
roads
buildings
other
both building and
road (the existence
of such conflict
areas is due to the
fact the two 2-class
SVM are trained
separately)

17. Conclusion
The ORFEO ToolBox has been considered as a basic component in our
application of map updating within the ARMURS project.
The provided image segmentation and classification functions speeded up the
implementation and test of the approach.
As far as the demonstrator is concerned, the integrated file formats for image
access and vector read are important assets.
We are also currently considering the potential of the recent OTB application
Urban Area Extraction (from OTB 3.0) as a component on which to base building
and road extraction.