4 September 1998 Statistical approach to multichannel spatial modeling for the detection of minelike targets
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Abstract
We present a statistically-based method for the enhancement and detection of mines and mine-like targets, in multi-channel imagery. Standard approaches to such multi-channel image processing take advantage of the correlation across channels within a pixel, but typically do not exploit the spatial dependency between pixels. This work aims to construct appropriate spatial statistical models for multi-channel mine imagery and apply these models to allow both image enhancement as well as direct and improved detection of anomalies (i.e., targets) in such data. We base the method on a Markov Random Field (MRF) model that incorporates a priori information about both the target's and the background's spatial characteristics. In particular, we find a Maximum A Posterior (MAP) detector of mine targets in background under the prior assumption target pixels are locally spatially dependent. We implement our algorithm on polarimetric and thermal data obtained from the Remote Minefield Detection System (REMIDS), with favorable results compared to a Maximum Likelihood (ML) detector that performs detections on a pixel-by-pixel basis, i.e. without spatial correlation.
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Robert A. Weisenseel, William Clement Karl, David A. Castanon, Charles A. DiMarzio, "Statistical approach to multichannel spatial modeling for the detection of minelike targets", Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); doi: 10.1117/12.324152; https://doi.org/10.1117/12.324152
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