While hyperspectral imaging systems are increasingly used in remote sensing and offer enhanced scene characterization relative to univariate and multispectral technologies, it has proven difficult in practice to extract all of the useful information from these systems due to overwhelming data volume, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. The need exists for the ability to perform rapid and comprehensive data exploitation of remotely sensed hyperspectral imagery. To address this need, this paper describes the application of a fast and rigorous multivariate curve resolution (MCR) algorithm to remotely sensed thermal infrared hyperspectral images. Employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, it is demonstrated that MCR can successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. We take a semi-synthetic approach to obtaining image data containing gas plumes by adding emission gas signals onto real hyperspectral images. MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of an ammonia gas plume component added near the minimum detectable quantity.
Chemical and physical materials-aging processes can significantly degrade the long-term performance reliability of dormant microsystems. This degradation results from materials interactions with the evolving microenvironment by changing both bulk and interfacial properties (e.g., mechanical and fatigue strength, interfacial friction and stiction, electrical resistance). Eventually, device function is clearly threatened and as such, these aging processes are considered to have the potential for high (negative) consequences. Sandia National Laboratories is developing analytical characterization methodologies for identifying the chemical constituents of packaged microsystem environments, and test structures for proving these analytical techniques. To accomplish this, we are developing a MEMS test device containing structures expected to exhibit dormancy/analytical challenges, extending the range of detection for a series of analytical techniques, merging data from these separate techniques for greater information return, and developing methods for characterizing the internal atmosphere/gases. Surface analyses and data extraction have been demonstrated on surfaces of various geometries with different SAMS coatings, and gas analyses on devices with internal free volumes of 3 microliters have also been demonstrated.
In this paper, we describe the use of linear unmixing algorithms to spatially and spectrally separate fluorescence emission signals from fluorophores having highly overlapping emission spectra. Hyperspectral image data for mixtures of Nile Blue and HIDC Iodide in a methanol/polymer matrix were obtained using the Information-efficient Spectral Imaging sensor (ISIS) operated in its Hadamard Transform mode. The data were analyzed with a combination of Principal Components Analysis (PCA), orthogonal rotation, and equality and non-negativity constrained least squares methods. The analysis provided estimates of the pure-component fluorescence emission spectra and the spatial distributions of the fluorophores. In addition, spatially varying interferences from the background and laser excitation were identified and separated. A major finding resulting from this work is that the pure-component spectral estimates are very insensitive to the initial estimates supplied to the alternating least squares procedures. In fact, random number starting points reliably gave solutions that were effectively equivalent to those obtained when measured pure-component spectra were used as the initial estimates. While our proximate application is evaluating the possibility of multivariate quantitation of DNA microarrays, the results of this study should be generally applicable to hyperspectral imagery typical of remote sensing spectrometers.