Adaptive Spatial Sampling Schemes for the Detection of Minefields in Hyperspectral Imagery

摘要

Often in hyperspectral overhead land mine imagery, there exists clutter with similar spatial and spectral characteristics to those of land mines. However groups of clutter features are rarely related spatially in the same way that groups of mines are related. For this reason, recognition of field patterns in overhead land mine imagery is critical to the detection of mine fields. The material presented here addresses means by which to spatially sample overhead hyperspectral imagery for the accentuation of mine field patterns. Our initial approach is to assume that the mines are laid out in a particular field pattern. We then search for spectral anomalies that are spatially distributed according to such a pattern. For this purpose, we utilize an RX detector with locally estimated mean and covariance matrix. We then use the pattern to predict the locations of additional mines. These locations provide us with search regions for the use of a second anomaly detector, in this case we use an anomaly detector based upon an eigenspace separation transform. Examples are provided using LWIR imagery.