Detecting Mines in Minefields With Linear Characteristics

We consider the problem of detecting minefields using aerial images. A first stage of image processing has reduced the image to a set of points, each one representing a possible mine. Our task is to decide which ones are actual mines. We assume that the minefield consists of approximately parallel r...

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Bibliographic Details
Published inTechnometrics Vol. 44; no. 1; pp. 34 - 44
Main Authors Walsh, Daniel C. I, Raftery, Adrian E
Format Journal Article
LanguageEnglish
Published Alexandria Taylor & Francis 01.02.2002
The American Society for Quality and The American Statistical Association
American Society for Quality
Subjects
Air forces
Algorithms
Bayesian inference
Classification
Datasets
False positive errors
Image analysis
Image processing
Jumping
Markov analysis
Markov chain Monte Carlo
Markov chains
Military air facilities
Mining
Noise
Parametric models
Spatial point process
Spatial points
Unmanned aerial vehicles
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Summary:We consider the problem of detecting minefields using aerial images. A first stage of image processing has reduced the image to a set of points, each one representing a possible mine. Our task is to decide which ones are actual mines. We assume that the minefield consists of approximately parallel rows of mines laid out according to a probability distribution that encourages evenly spaced, linear patterns. The noise points are assumed to be distributed as a Poisson process. We construct a Markov chain Monte Carlo algorithm to estimate the model and obtain posterior probabilities for each point being a mine. The algorithm performs well on several real minefield datasets.
ISSN:0040-1706
1537-2723
DOI:10.1198/004017002753398308