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10 June 2005 Semantic risk estimation of suspected minefields based on spatial relationships analysis of minefield indicators from multi-level remote sensing imagery
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Abstract
This paper presents semantic risk estimation of suspected minefields using spatial relationships of minefield indicators extracted from multi-level remote sensing. Both satellite image and pyramidal airborne acquisitions from 900m to 30m flying heights with resolutions from 1m to 2cm resolutions are used for identification of minefield indicators. R-Histogram [1] is a quantitative representation of spatial relationship between two objects in an image. Eight spatial relationships can be generated: 1) LEFT OF, 2) RIGHT OF, 3) ABOVE, 4) BELOW, 5) NEAR, 6) FAR, 7) INSIDE, 8) OUTSIDE. R-Histogram semantics are first generated from selected indicators and metrics such as topological proximity and directional relationships are trained for soft classification of risk index (normalized as 0-1). We presented a framework of how semantic metadata generated from remote sensing images are used in risk estimation. The resultant risk index identified seven out of twelve mine accidents occurred at high risk region. More importantly, comparison with ground truth obtained after mine clearance show that three out of the four identified pattern minefields falls into the area estimated at very high risk. A parcel-based per-field risk estimation can also be easily generated to show the usefulness of the risk index.
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Jonathan Cheung-Wai Chan, Hichem Sahli, and Yuhang Wang "Semantic risk estimation of suspected minefields based on spatial relationships analysis of minefield indicators from multi-level remote sensing imagery", Proc. SPIE 5794, Detection and Remediation Technologies for Mines and Minelike Targets X, (10 June 2005); https://doi.org/10.1117/12.603450
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