Exploiting stochastic partitions for minefield detection

In many applications one wishes to perform an analysis of the homogeneity of a point process, often as a precursor to more advanced analysis. In general, rejection of the null hypothesis of homogeneity may imply a requirement for further analysis. In remote sensing for minefield detection, for example, homogeneity may correspond to the 'no minefield' case while regions of nonhomogeneity warrant closer inspection. This paper considers a version of the spatial scan process which uses stochastic and disjoint scan regions. The associated test for nonhomogeneity has the potential for improved power over conventional alternative sin applications where the point process is embedded in a general random field. Specifically, when the locations of any subregions of nonhomogeneity in the point process correspond to regions in the underlying field which can be segmented as distinct from their surroundings, the test derived here is recommended. The application to the detection of point clusters in gray-scale imagery, particularly minefields in multispectral imagery, is investigated.