Bayesian hierarchical analysis of minefield data

Noel A. C. Cressie; Andrew B. Lawson

doi:10.1117/12.324262

4 September 1998 Bayesian hierarchical analysis of minefield data

Noel A. C. Cressie, Andrew B. Lawson

Proceedings Volume 3392, Detection and Remediation Technologies for Mines and Minelike Targets III; (1998) https://doi.org/10.1117/12.324262
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

Based on remote sensing of a potential minefield, point locations are identified, some of which may not be mines. The mines and mine-like objects are to be distinguished based on their point patterns, although it must be emphasized that all we see is the superposition of their locations. In this paper, we construct a hierarchical spatial point-process model that accounts for the different patterns of mines and mine-like objects and uses posterior analysis to distinguish between them. Our Bayesian approach is applied to COBRA image data obtained from the NSWC Coastal Systems Station, Dahlgren Division, Panama City, Florida.

Citation Download Citation

Noel A. C. Cressie and Andrew B. Lawson "Bayesian hierarchical analysis of minefield data", Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); https://doi.org/10.1117/12.324262

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