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Detecting man-made structure changes to assist geographic data producers in planning their update strategy

par Leignel, Christine ;Hanson, Emilie ;Wolff, Eléonore ;Caelen, Olivier ;Bontempi, Gianluca ;Debeir, Olivier ;Leloup, Thierry ;Warzée, Nadine ;Simler, Christophe;Beumier, Charles M.
Référence The international archives of the photogrammetry, remote sensing and spatial information sciences, 38, page (26-33)
Publication Publié, 2010

Article révisé par les pairs

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Résumé :

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.