Detection of chemical plumes in hyperspectral data is a problem having solutions that focus on spectral information. These solutions neglect the presence of the spatial information in the scene. The spatial information is exploited in this work by assignment of prior probabilities to neighborhood configurations of signal presence or absence. These probabilities are leveraged in a total probability approach to testing for signal presence in a pixel of interest. The two new algorithms developed are named spatial information detection enhancement (SIDE) and bolt-on SIDE (B-SIDE). The results are explored in comparison to the clutter matched filter (CMF), a standard spectral technique, and to several supervised machine learning techniques. The results show a great improvement of SIDE over these other techniques, in some cases showing the poorest performance of the SIDE filter being much better than the CMF at its best.