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

• Published in: arXiv.org
• Main Authors: Bandstra, M S, Quiter, B J, Salathe, M, Bilton, K J, Curtis, J C, Goldenberg, S, Joshi, T H Y
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 19.11.2021
• Subjects: Algorithms; Feature extraction; Gamma ray sources; Physics - Applied Physics; Physics - Data Analysis, Statistics and Probability; Physics - Instrumentation and Detectors; Sensors; Spectra; Urban areas
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2111.10463

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.