Detecting mines and minelike objects in highly cluttered multispectral aerial images by means of mathematical morphology
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Automatic mine detection is a critical issue in battle field management. This is expected to lead to better technologies that provide accurate and reliable detection of mines embedded in clutter. In this paper, we review a procedure for automatic mine detection in multispectral data provided by the Coastal Battlefield Reconnaissance and Analysis (COBRA) program. Our procedure is essentially a two-step method that employs the Maximum Noise Fraction (MNF) transform, a powerful enhancement tool for multispectral data, combined with nonlinear morphological operators that do the actual detection. Mathematical morphology is also used to account for the critical step of clutter estimation required by the MNF transform. Results obtained with available, truthed data, show the high success of the proposed method in meeting performance requirements. A low number of midsections is observed, whereas only a small number of false alarms is introduced by the algorithm. The results are better than the ones obtained by means of a constant false alarm rate (CFAR) algorithm provided along with the data. 2003 Copyright SPIE - The International Society for Optical Engineering.
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Detection and Remediation Technologies for Mines and Minelike Targets III