Pattern recognition of amorphously shaped objects such as gas plumes, oil spills, or epidemiological spread is difficult because there is no definite shape to match. We consider detection of such amorphously shaped objects using a neighborhood model which operates on a concept of loose spatial contiguity: there is a significant probability that a pixel surrounded by the object of interest itself contains that object of interest, and boundaries tend to be smooth. These assumptions are distilled into a single-parameter prior probability model to use in a maximum a posteriori hypothesis test. The method is evaluated against synthetic data generated from hyperspectral imagery and DIRSIG simulation results. These tests indicate significant improvement on the ROC curves.