The use of Orfeo Toolbox in the Context of Map Updating

Christophe SimlerRoyal Military Academy

Brussels, Belgium

Main part of the ARMURS projectVHR satellite image (Ikonos or Quickbird) or aerial image RGB

XS pansharpenedmultispectral pixel description and mean shift segmentation

segmented image

raster of an old vectorgeographical database

region feature extraction and SVM classification

classified image (road/building/other)

change map (roads and buildings)

comparaison

pansharpening

database update

Softwares

-ORFEO Toolbox

-Development

-Proprietary code

-Open source code

FreewareExtensibleHandle most image format (use GDAL)Image processing for remote sensing

Main part of the ARMURS projectVHR satellite image (Ikonos or Quickbird) or aerial image RGB

XS pansharpenedmultispectral pixel description and mean shift segmentation

segmented image

raster of an old vectorgeographical database

region feature extraction and SVM classification

classified image (road/building/other)

change map (roads and buildings)

comparaison

pansharpening

database update

Main part of the ARMURS projectVHR satellite image (Ikonos or Quickbird) or aerial image RGB

XS pansharpenedmultispectral pixel description and mean shift segmentation: otb::MeanShiftVectorImageFilter

segmented image

raster of an old vectorgeographical database

region feature extraction and SVM classification: otb::SVMModel and otb::SVMClassifier

classified image (road/building/other)

change map (roads and buildings)

comparaison

pansharpening: otb::SimpleRcsPanSharpeningFusionImageFilter

database update

Mean shift segmentation results

Roads and buildings aregenerally preciselyextracted

Part of an Ikonos satelliteimage in the region ofJodoigne (Belgium)

Régions feature extraction

The regions obtained from the segmentation are described by the followingfeature vector:

areaeccentricity mean R mean G mean Bmean NIR

Image classification

The feature vectors are classified into classes « roads », « building » or « other »

Support Vector Machine (SVM)

Training set

Our training set is composed of about 1000 mean shift regions manually assignedto class « road », « building » or « other »

two componants of ourfeature vector:

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

Training

training setnew couple of parameter values

decision boundaries

test set

learning

balanced loss=

INPUT : training set (road/other or building/other)

optimal parameter values(loop)

learning

OUTPUT : decision boundaries

permutation

INPUT : 2D grid value for the 2 parameters to tune

FPVN

FP

FNVP

FN

++

+=

1- optimisation of the two parameters by cross validation2- learning on the whole set3- classifier performance quantification

Optimal tuning

Energie to minimise

Optimal tuning

Minimisation with coarse-to-fine approach

Optimal tuning

Minimisation with coarse-to-fine approach

Classification: SVM input image

Part of an aerial RGBimage of a region ofBruxelles (Belgium)

SVM Classification results

Overlap of the two2-class SVM classification results

roads

buildingsother

both building androad (the existenceof such conflict areas is due to the fact the two 2-classSVM are trained separately)

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 applicationUrban Area Extraction (from OTB 3.0) as a component on which to base building and road extraction.