In the last decade, several humanitarian demining actions have acknowledged the role of remote sensing as a useful tool, able to enhance the productivity, cost-effectiveness and safety of ground-based minefield detection methods [1] [2] [3] [4]. Air- and s

Remote sensing minefield area reduction: Model-based approaches for the extraction of minefield indicators

(1) Vrije Universiteit Brussel, VUB-ETRO, Peinlaan, 2 1050 Brussels, Belgium, Belgium

(2) Interuniversitair Micro-Elektronica Centrum, IMEC-ETRO, VUB-ETRO,

In the last decade, several humanitarian demining actions have acknowledged the role of remote sensing as a useful tool, able to enhance the productivity, cost-effectiveness and safety of ground-based minefield detection methods [1] [2] [3] [4]. Air- and space-borne platforms, equipped with multiple sensors, can quickly and safely scan large inaccessible areas. Remote sensing can contribute to the mapping and identification of suspected areas, and eventually to the determination of the minefield boundaries, through the extraction of suitable direct and indirect minefield indicators. The identification of image indicators, in combination with collected ancillary information, prior knowledge/intelligence, can assist in conventional General Mine Action Assessment and Technical Surveys prior to a clearance (demining) campaign and implicitly highlight areas of high risk.

Minefield indicators are conventionally identified via visual image interpretation, by experts with in-depth field knowledge and experience. This process is carried out either through spontaneous recognition, through recollection of object features (such as color, texture, size, shape, shadow), or using a process of logical inference. Despite its effectiveness, visual interpretation however can become intractable in cases of huge volumes of data, where automatic image analysis techniques could be more effective. However systems for automatic image analysis should detect visually perceivable image entities that carry semantic interpretation for an expert observer.

In this paper, we demonstrate the potential of computer vision techniques, which meet this requirement, for the automated extraction of minefield indicators in rural areas with heavily cluttered environments. We have developed a series of model-based methods for the detection of regions/objects/structures with a versatile appearance in terms of size, shape and spectral/textural signatures. The investigated techniques, classified according to each considered type of minefield indicator, are listed.

Linear structures: Minefield indicators of this type include elongated structures related to warfare activities (e.g. trenches), roads and paths that designate safe passage areas, protection walls, rivers and geological erosions. We have developed a model-based approach for the identification of linear structures, which follows assumptions concerning their geometric and radiometric properties [5]. During a local analysis step, the detection of elongated structures is performed by applying a series of morphological filters. The main axis of the extracted elongated structures is determined by applying the watershed A. Katartzis(1), I. Vanhamel(1), J. C-W Chan(2,1),H. Sahli(1,2)