Proc. SPIE. 11010, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX
KEYWORDS: Signal to noise ratio, Hyperspectral imaging, Statistical analysis, Detection and tracking algorithms, Scanners, Imaging spectroscopy, Data processing, Photonics, Stochastic processes, Line scan image sensors
Hyperspectral imaging (HSI) has become increasingly popular for sensing in defense, commercial, and academic research for its ability to acquire vast amounts of information, relatively quickly, at stand-off distances. As such, the need for rapid or near-real time data reduction is becoming more evident especially when immediate knowledge of the area under investigation is required such as in contested areas, the scene of natural disasters, and other similar scenarios. While analysis of the underlying spectral information may provide specific information about materials present, in HSI determining an anomaly can be just as informative in scenarios such as CB detection for avoidance. Therefore, a rapid, real-time HSI anomaly detection algorithm is merited. In this paper, we present work towards an algorithm for near-real time anomaly detection utilizing higher-order statistics and, in particular, implications due to changes in skewness and kurtosis, the 3rd and 4th central moments. We demonstrate using a visible-SWIR hyperspectral line scanner that anomalies (thiodiglycol and acetaminophen) can be detected in data that is updated to simulate real-time analysis. Changing spectral features result in changes in the probability density function, and can be specifically realized with comparisons of higher order statistics (i.e. skewness and kurtosis), thereby reducing a full spectral analysis at each voxel to a comparison of two values at each pixel. This paper explores utilizing this concept as a means for anomaly detection, evaluating different surfaces that an analyte may be present on, and lastly presents work towards automated background updates for anomaly detection on dynamic surfaces.
Super resolution chemical imaging can provide high spatial resolution images that contain chemically specific information. Additionally, using a technique such as Raman scattering provides molecular specific information based on the inherent vibrations within the analyte of interest. In this work, commercially available fiber bundle arrays (1mm diameter) consisting of 30,000 individual fiber elements (4μm diameter) that are then modified to obtain surface enhanced Raman scatter are employed. This allows for the visualization of vibrational information with high spatial (i.e. sub-diffraction limited) resolution over the 30,000 individual points of interrogation covering a total imaging diameter of approximately 20μm in a non-scanning format. Using these bundles, it has been shown that dithering can increase the spatial resolution of the arrays further by obtaining several sub-element shifted images. To retain the spatial resolution of such images, cross talk associated with these tpared bundles must be kept at a negligible level.
In this paper, a study of luminescent particles isolated in individual fiber wells has been performed to characterize the cross talk associated with these fiber bundles. Scanning-electron microscope (SEM) images provide nanometric characterization of the fiber array, while luminescent signals allow for the quantitation of cross talk between adjacent fiber elements. From these studies negligible cross-talk associated with both untapered and tapered bundles was found to exist.
This review describes the recent advances in plasmonic nanostructures for various sensing applications. In particular, significant advances in surface-enhanced Raman, surface plasmon resonance, and metal-enhanced fluorescence-sensing methodologies associated with the introduction of plasmonic nanostructures, made over the past decade, are highlighted. Plasmonic properties of the various nanostructures employed for each sensing technique are also tabulated to provide a systematic overview of the state-of-the-art in each sensing field. This review is not intended to be a comprehensive compilation of the literature but rather a critical review of the recent significant advances in plasmonic nanostructures for each sensing regime.
Super-resolution chemical imaging via Raman spectroscopy provides a significant ability to simultaneously or pseudosimultaneously monitor numerous label-free analytes while elucidating their spatial distribution on the surface of the sample. However, spontaneous Raman is an inherently weak phenomenon making trace detection and thus superresolution imaging extremely difficult, if not impossible. To circumvent this and allow for trace detection of the few chemical species present in any sub-diffraction limited resolution element of an image, we have developed a surface enhanced Raman scattering (SERS) coherent fiber-optic imaging bundle probe consisting of 30,000 individual fiber elements. When the probes are tapered, etched and coated with metal, they provide circular Raman chemical images of a sample with a field of view of approximately 20μm (i.e. diameter) via the array of 30,000 individual 50 nm fiber elements. An acousto-optic tunable filter is used to rapidly scan or select discrete frequencies for multi- or hyperspectral analysis. Although the 50nm fiber element dimensions of this probe inherently provide spatial resolutions of approximately 100nm, further increases in the spatial resolution can be achieved by using a rapid dithering process. Using this process, additional images are obtained one-half fiber diameter translations in the x- and y- planes. A piezostage drives the movement, providing the accurate and reproducible shifts required for dithering. Optimal probability algorithms are then used to deconvolute the related images producing a final image with a three-fold increase in spatial resolution. This paper describes super-resolution chemical imaging using these probes and the dithering method as well as its potential applications in label-free imaging of lipid rafts and other applications within biology and forensics.