On Detecting Mines And Minelike Objects In Highly Cluttered Multispectral Aerial Images By Means Of Mathematical Morphology

Automatic mine detection is a critical issue in battlefield 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 misdetections 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.