Correlations between Panoramic Imagery and Gamma-Ray Background in an Urban Area

TL;DR

This study explores how panoramic imagery correlates with gamma-ray background levels in urban environments, aiming to enhance detection algorithms by incorporating environmental context.

Contribution

It introduces a method to relate panoramic image features to gamma-ray background data using a linear model, improving understanding of environmental influences on background radiation.

Findings

Strong correlation between image features and gamma-ray background components

Sky and building solid angles influence gamma-ray background levels

Potential for context-aware radiological detection algorithms

Abstract

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.