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Object‐based image analysis for detecting indicators of mine presence to support suspected hazardous area re‐delineation

par Vanhuysse, Sabine ;Hoelbling, Daniel;Friedl, Barbara;Hanson, Emilie ;Krtalic, Andrija;Hagenlocher, Michael;Racetin, Ivan;Wolff, Eléonore
Référence South-Eastern European Journal of Earth Observation and Geomatics, 3, 2, page (525-529)
Publication Publié, 2014

Article révisé par les pairs

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Résumé :

In the framework of Mine Action, the extent of Suspected Hazardous Areas (SHAs) is often overestimated. This study investigates the potential of Object‐Based Image Analysis (OBIA) for extracting Indicators of Mine Presence (IMP) to support a more precise delineation of SHAs, with the aim of ensuring an optimal use of demining resources. The study area is situated in the Svilaja mountain range in Croatia. Using 3K colour aerial photographs, we implemented two approaches for the extraction of dry stone walls located in an area that displays traces of military activities. The first approach uses object‐based class modelling, which describes an iterative process of segmentation and classification. The second approach implements supervised learning techniques based on advanced statistical classification methods, i.e. Support Vector Machines, Random Forests and Recursive Partitioning. The results are compared, the strengths and limitations of both approaches are discussed, and perspectives for further improvements are considered.