Adaptive multispectral CFAR detection of land mines

An automatic target detection algorithm which exploits spectral and spatial signatures of mines is described. Key features of this approach include the ability to adapt to unknown or changing background statistics and the capability to operate with unknown spectral signatures. Preliminary results of applying this algorithm for surface mine detection in video-based multispectral imagery covering the 400-900 nm region are presented. Tests on actual airborne data collected during 1992, 1993, and 1994 show that at 8-inch ground resolution (with 4x over-sampling), 12-inch diameter circular mines can be discriminated from natural backgrounds with a probability of detection around 85% with 3-4 false alarms per image in a relatively harsh clutter environment. This capability has been shown to be sufficient to meet COBRA minefield requirements during preliminary system testing.