  When searching for radiological sources in an urban area, a vehicle-borne
detector system will often measure complex, varying backgrounds primarily from
natural gamma-ray sources. Much work has been focused on developing spectral
algorithms that retain sensitivity and minimize the false positive rate even in
the presence of such spectral and temporal variability. However, information
about the environment surrounding the detector system might also provide useful
clues about the expected background, which if incorporated into an algorithm,
could improve performance. Recent work has focused on extensive measuring and
modeling of urban areas with the goal of understanding how these complex
backgrounds arise. This work presents an analysis of panoramic video images and
gamma-ray background data collected in Oakland, California by the Radiological
Multi-sensor Analysis Platform (RadMAP) vehicle. Features were extracted from
the panoramic images by semantically labeling the images and then convolving
the labeled regions with the detector response. A linear model was used to
relate the image-derived features to gamma-ray spectral features obtained using
Non-negative Matrix Factorization (NMF) under different regularizations. We
find some gamma-ray background features correlate strongly with image-derived
features that measure the response-adjusted solid angle subtended by sky and
buildings, and we discuss the implications for the development of future,
contextually-aware detection algorithms.
