With increasing understanding and availability of nuclear technologies, and increasing persuasion of nuclear technologies by several new countries, it is increasingly becoming important to monitor the nuclear proliferation activities. There is a great need for developing technologies to automatically or semi-automatically detect nuclear proliferation activities using remote sensing. Images acquired from earth observation satellites is an important source of information in detecting proliferation activities. High-resolution remote sensing images are highly useful in verifying the correctness, as well as completeness of any nuclear program. DOE national laboratories are interested in detecting nuclear proliferation by developing advanced geospatial image mining algorithms. In this paper we describe the current understanding of geospatial image mining techniques and enumerate key gaps and identify future research needs in the context of nuclear proliferation.Index Terms-Nuclear proliferation, low-level features, semantic classification, geospatial ontology GEOSPATIAL IMAGE MININGIncreasing resolution, volume, and availability of remote sensing imagery made it possible to accurately identify key geospatial features and their changes over time. Recent studies have shown the usefulness of remote sensing imagery for monitoring nuclear safeguards and proliferation activities [1]. Classification is one of the widely used technique for extracting thematic information. Classification is often performed *Contact: bhaduribl@ornl.gov on per-pixel basis; however proliferation detection requires identification of complex objects, patterns and their spatial relationships. One key distinguishing feature as compared to traditional thematic classification is that the objects and patterns that constitute a nuclear facility have interesting spatial relationships (metric, topological, etc) among themselves. These limitations are clearly evident from Figure 1. Classification technology is mature for extracting thematic classes such as buildings, forest, crops, etc. However, such thematic labels are not enough to capture the fact that the given image contains a nuclear power plant. What is missing is the fact that the objects (e.g., switch yard, containment building, turbine building, cooling towers) and their spatial relationships (arrangements or configurations) are not captured in traditional thematic classification. In addition, traditional image analysis approaches mainly exploit low-level image features (such as, color and texture and, to some extent, size and shape) and are oblivious to higher level descriptors and important spatial (topological) relationships without which we can not accurately discover these complex objects or higher level semantic concepts. One stumbling block in exploiting such relationships is in the description of compound objects and the spatial relationships among the object constituents. Therefore, for effective utilization of remote sensing imagery, first it is important to identify key concepts that des...

A fluorochlorozirconate (FCZ) glass-ceramic containing orthorhombic barium chloride crystals doped with divalent europium was evaluated for use as a storage phosphor in gamma-ray imaging. X-ray diffraction and phosphorimetry of the glassceramic sample showed the presence of a significant amount of orthorhombic barium chloride crystals in the glass matrix. Transmission electron microscopy and scanning electron microscopy were used to identify crystal size, structure, and morphology. The size of the orthorhombic barium chloride crystals in the FCZ glass matrix was very large,~0.5-0.7 lm, which can limit image resolution. The FCZ glass-ceramic sample was exposed to 1 MeV gamma rays to determine its photostimulated emission characteristics at high energies, which were found to be suitable for imaging applications. Test images were made at 2 MeV energies using gap and step wedge phantoms. Gaps as small as 101.6 lm in a 440 stainless steel phantom were imaged using the sample imaging plate. Analysis of an image created using a depleted uranium step wedge phantom showed that emission is proportional to incident energy at the sample and the estimated absorbed dose. The results showed that the sample imaging plate has potential for gamma-ray-computed radiography and dosimetry applications.

No abstract

Image data management in the semiconductor manufacturing environment is becoming more problematic as the size of silicon wafers continues to increase, while the dimension of critical features continues to shrink. Fabricators rely on a growing host of image-generating inspection tools to monitor complex device manufacturing processes. These inspection tools include optical and laser scattering microscopy, confocal microscopy, scanning electron microscopy, and atomic force microscopy. The number of images that are being generated are on the order of 20,000 to 30,000 each week in some fabrication facilities today. Manufacturers currently maintain on the order of 500,000 images in their data management systems for extended periods of time. Gleaning the historical value from these large image repositories for yield improvement is difficult to accomplish using the standard database methods currently associated with these data sets (e.g., performing queries based on time and date, lot numbers, wafer identification numbers, etc.). Researchers at the Oak Ridge National Laboratory have developed and tested a content-based image retrieval technology that is specific to manufacturing environments. In this paper, we describe the feature representation of semiconductor defect images along with methods of indexing and retrieval, and results from initial field-testing in the semiconductor manufacturing environment.

No abstract