The development of a computer vision algorithm for use with 3D voxel space radar imagery is observed in this thesis. The goal is to detect explosive hazards present in 3D synthetic aperture radar (SAR) image data. The algorithm consists of three primary stages; a precreener to find areas of interest, clustering for labeling distinct areas, and a classifier. The performance between multiple prescreener methods are compared when using a heuristic classifier. Finally, a convolutional neural network (CNN) is used as a classifier stage and a comparison between a deep network, a shallow network, and human experts is conducted.