Detecting Man-made Structure Changes to Assist Geographic Data Producers in Planning Their Update Strategy

Topographical data producers are currently confronted with the need for a faster updating method. Indeed, this need was assessed at the first stage of the ARMURS project by surveying several Belgian and international mapping agencies. The aim of the project is to build a demonstrator to assist data producers in planning the update of their topographic database more efficiently relying on remote sensing images, together with socio-economic and demographic data. At a regional scale, the man-made structure changes extracted by the ETATS module on a SPOT5 panchromatic image will be fused with a change analysis on socio-demographic data. At a local scale, as regards areas of predicted changes, the older databases are compared with recent very high-resolution satellite or aerial images in order to detect changes in man-made structures. Changes are detected by comparing an object-oriented classified VHR image to a simplified version of the old database. This object-oriented approach consists in a segmentation followed by a classification. Three segmentation methods (Watershed Assembly, Graph Cut, Mean Shift) were implemented and compared to the one of a commercial software (Definiens); indices were proposed to assess the quality of these segmentations. Features are selected either according to a visual interpretation formalised into an interpretation key, or by quantitative methods such as the forward Jeffries-Matusita distance or mutual information criteria (mRMR); selections are compared. By using a common framework (images, training set and validation set), existing classification methods available in Definiens and in R are compared. A final step of change detection gives us preliminary results.