PNNL

Abstract

PNNL is developing and evaluating radiographic image analysis techniques (active and passive) for verifying sensitive objects in a material control or warhead counting regime in which sensitive information may be acquired and processed behind an information barrier. Since sensitive image information cannot be present outside the information barrier, these techniques are necessary to extract features from the full images and reduce them to relevant parameters (attributes) of the inspected items. This evaluation can be done behind the information barrier, allowing for reporting and storage of non-sensitive attributes only. Several advances have been made to radiographic object verification algorithms, in the areas of spectral imaging for passive detectors and estimation of material density in active radiographic images. Both of these advances are pertinent in an arms control context. While passive radiographic images produced by previous work may be evaluated for the presence of emissive objects, approaches which leverage the spectroscopic potential of the detectors allow a much greater discrimination of SNM from background and other sources. Spectral passive imaging approaches to warhead discrimination and counting include specific materials and geometric arrangement localization, as well as “spectral difference” metrics which group regions with similar spectra together. These approaches may improve resolution for discrimination between materials in addition to locating SNM within surrounding shielding and/or structural elements. Previous work by our group has developed the capability to discern material density and composition in radiographic images by examining the edge transition characteristics of objects. The material construction of an object can be investigated in this way. In a weapons counting or discrimination context, unknown occultation of objects of interest, as well as additional elements of warhead construction, construction materials of varying geometry and makeup and various angles of radiograph are expected to impact algorithm performance. Advances in material discrimination algorithms are presented as a mechanism to help make these approaches robust to these sorts of variations.