Incident angle dependencies of LADAR reflection depend on bulk material reflectivity and surface texture properties that can be exploited for surface identification. In this paper, surface identification via multiband LADAR reflected radiance is assessed using the nonconventional exploitation factors data system database. A statistics-based dimension reduction algorithm, stochastic neighborhood embedding (t-SNE), is used to separate the data clouds resulting from the monostatic LADAR reflected radiance and corresponding band ratios. The application of t-SNE to multiband reflected radiance effectively separates the data clouds, making surface identification via multiband LADAR reflectance possible in the presence of unknown incident angle dependencies and uncertainties. It is demonstrated that, for both the multiband monostatic reflected radiance and band ratios, the application of t-SNE mapping yields a significant improvement in surface identification from measurements with unknown or varied incident angles.

The presence of cirrus clouds introduces complex heating and cooling effects on the atmosphere and can also interfere with remote sensing from satellite-based sensors or from high-altitude aircraft. Detection of cirrus clouds thus provides an opportunity for atmospheric correction to introduce accurate compensation to images of the earth’s surface. Previous work on detection and characterization of cirrus clouds have been based on observing spectral signatures on a spectral channel with significant water absorption, or calculation of radiant intensity ratios over a water band to a reference spectral channel. Our proposed approach is based on applying computational homology to characterize the topological properties of cirrus clouds. We utilize an application called JPLEX to study the persistent homology of multi-dimensional simplicial complexes built from available hyperspectral or multispectral data. The technique has been successfully applied to discriminate subtle features in high dimensional noisy data sets. Previous examples include anomaly detection in hyperspectral images. The analysis makes use of the entire multidimensional data set (not just one or a combination of spectral bands) which may offer advantages in discriminating among various cloud types in a scene, as well as determining other characteristics of cirrus clouds such as altitude and thickness. Our initial computational experiment with an AVIRIS scene has demonstrated that JPLEX is able to discriminate between cumulus and cirrus clouds.

The high energy (< 15 MeV) incident polychromatic gamma-ray spectrum and energy-resolved photon attenuations in steel, obtained via EGS/BEAM Monte Carlo, were applied to derive beam hardening correction in steel cylinders and pipes. Monte Carlo simulated pencil beam projections were processed using latch bit filters to isolate exiting primary photons. The beam hardening correction was applied to these projections, which were then interpolated to a uniform grid. Filtered back projections of the raysums with and without the beam hardening correction were compared. It was demonstrated that beam hardening artifacts can be successfully removed for steel structures.

Light reflection from a surface is described by the bidirectional reflectance distribution function (BRDF). In this paper, BRDF effects in reflection tomography are studied using modeled range-resolved reflection from well-characterized geometrical surfaces. It is demonstrated that BRDF effects can cause a darkening at the interior boundary of the reconstructed surface analogous to the well-known beam hardening artifact in x-ray transmission computed tomography (CT). This artifact arises from reduced reflection at glancing incidence angles to the surface. It is shown that a purely Lambertian surface without shadowed components is perfectly reconstructed from range-resolved measurements. This result is relevant to newly fabricated carbon nanotube materials. Shadowing is shown to cause crossed streak artifacts similar to limited-angle effects in CT reconstruction. In tomographic reconstruction, these effects can overwhelm highly diffuse components in proximity to specularly reflecting elements. Diffuse components can be recovered by specialized processing, such as reducing glints via thresholded measurements.

We describe a new visible-near infrared short-wavelength infrared (VNIR-SWIR) atmospheric correction method for multi- and hyperspectral imagery, dubbed QUAC (Quick Atmospheric Correction) that also enables retrieval of the wavelength-dependent optical depth of the aerosol or haze and molecular absorbers. It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the spectral standard deviation of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially spectrally flat. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for real-time applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. QUAC is applied to atmospherically correction several AVIRIS data sets and a Landsat-7 data set, as well as to simulated HyMap data for a wide variety of atmospheric conditions. Comparisons to the physics-based Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) code are also presented.

A method of optimizing the selection of spectral channels in a spectral-spatial remote sensor has been developed that is applicable to the design of multispectral, hyperspectral and ultra spectral resolution sensors. The approach is based on an end member analysis technique that has been refined to select the most information dense channels. The algorithm operates sequentially and at any step in the sequence, the channel selected is the most independent form all previously selected channels. After the channel selection process, highly correlated channels, which are contiguous to those selected, can be merged to form bands. This process increases the signal to noise for the new broader spectral bands. The resulting bands, potentially of unequal width and spacing, collect the most uncorrelated spectral information present in the data. The band selection provides a physical interpretation of the data and has applications in spectral feature selection and data compression.

Shadow-insensitive detection or classification of surface materials in atmospherically corrected hyperspectral imagery can be achieved by expressing the reflectance spectrum as a linear combination of spectra that correspond to illumination by the direct sum and by the sky. Some specific algorithms and applications are illustrated using HYperspectral Digital Imagery Collection Experiment (HYDICE) data.

We propose that the principles of relativistic quantum mechanics are incomplete for simultaneous measurement of non-commuting operators. Consistent joint measurement of incompatible observables at a single point in space-time requires that the system be in an entangled state with vacuum meters. We suggest that entagled simultaneous mesurement for noncommuting observables is the basis for the observed fermionic multiplets. This generalizes the standard spin representations for particles arising from Lorentz invariance. We show that operator entanglement for all quantum observables in the Poincare algebra, coupled with Fermi-Dirac statistics, mandates six fermions. We propose that the quark and lepton generations form a super-structure of the Poincare algebra based on the principles of entangled simultaneity. Mathematically, that super-structure is known as a Naimark extension. The required entanglement between particle generations for left-handed quarks is observed in the Cabbibo-Kobayashi-maskawa matrix. We show that the Naimark-extended von Neumann lattice is ditributive, thereby suggesting the principle of entangled simultaneity as a mechanism to avoid quantum non-locality. Keywords: enatangled simultaneous quantum measurement, Naimark extension, Lepton/quark generations. PACS Number: 03.65.BZ

We extend the theory of three-dimensional (3D) tomographic intensity modulated radiation therapy (IMRT). The geometry consists of two-dimensional modulated beams on a sphere centred in the tumour. The theory provides an efficient algorithm for computing beam modulation patterns that approximately 'reconstruct' the prescribed dose function. In this paper optimum beam numbers are estimated from dose function spherical harmonics using the 3D projection-slice theorem. An extension to three dimensions of the 'Bow Tie' criterion for beam numbers is derived. The effects of insufficient beam front sampling and beam numbers are characterized with a configuration-dependent matrix. Factors that independently increase beam numbers, such as tumour size and shape, are related to the spherical harmonic content in the dose function. Examples of tomographic IMRT reconstruction with a 3D concave tumour are given.

A three-dimensional tomographic reconstruction algorithm for an absorptive perturbation in tissue is derived. The input consists of multiple two-dimensional projected views of tissue that is backilluminated with diffuse photon density waves. The algorithm is based on a generalization of the projection-slice theorem and consists of depth estimation, image deconvolution, filtering, and backprojection. The formalism provides estimates of the number of views necessary to achieve a given spatial resolution in the reconstruction. The algorithm is demonstrated with data simulated to mimic the absorption of a contrast agent in human tissue. The effects of noise and uncertainties in the depth estimate are explored.

A 3D tomographic reconstruction algorithm for an absorptive perturbation in tissue is derived. The input consists of multiple 2D projected views of tissue that is back-illuminated with diffuse photon density waves (DPDWs). The algorithm is based on a generalization of the Projection-Slice Theorem and consists of depth estimation, image deconvolution, filtering, and backprojection. The formalism provides estimates of the number of views necessary to achieve a given spatial resolution in the reconstruction. The algorithm is demonstrated with data simulated to mimic the absorption of a contrast agent in human tissue. The effects of noise and uncertainties in the depth estimate are explored.

Scattered solar radiance from cirrus clouds has traditionally been detected over land at 1.37 mu m a wavelength that is ordinarily opaque to the surface due to water vapor absorption. We describe a new pairwise regression method for spectral imagery that retrieves cloud signals in the vicinity of a partially transmitting band, such as the 1.13 mu m band, over any type of spatially structured terrain. The method, which uses spatial filtering and linear regression to cancel the surface background, has been applied to several rural and urban AVIRIS scenes. With a single cloud or cloud layer in the scene, the 1.13 mu m and 1.37 mu m cloud signals are closely correlated. Since the two signals are absorbed differently by water vapor, the slope of the correlation plot indicates the column water vapor above the cloud and thus the approximate cloud altitude. The less strongly absorbed 1.13 mu m signal is closely related to the cloud optical thickness and can be used by itself or in combination with the 1.37 mu m signal to correct apparent surface reflectance spectra for cirrus cloud effects.

A new, state-of-the-art atmospheric correction algorithm for the solar spectral range has been developed based on the MODTRAN4 code. The primary data products are surface reflectance spectra, column water vapor maps and relative surface elevation maps. In addition, a radiance simulation tool, an automated visibility retrieval algorithm and a spectral "polishing" algorithm are included. Validations of retrievals have been carried out by analyzing data that encompass a variety of atmospheric and surface conditions. Some results and their implications for atmospheric correction and spectroscopy are discussed.

The inversion problem for γ-ray (≥1MeV) conformal radiotherapy is analyzed using the mathematics of tomographic reconstruction. It is shown that the delivered dose can be approximated by the dual attenuated x-ray transform of the filtered beam profile function. The number of intensity-modulated beams required for dose conformation to a tumor is derived. The sampling requirement is at most (2πr max W max + 5/2) beams for a 2D tomotherapy geometry, where r max and W max are the maximum spatial extent and frequency, respectively, of the radiation dose. We generalize this ‘Bow Tie’ solution to 3D, suggesting a sufficient beam number given by (Δω/2πW max )(2πr max W max +5/2)2, where Δω is the frequency resolution of the beam front modulation. The matrix inversion implicit in this bound suggests a criterion for beam orientation selection. Beam angles should be chosen such that the SVD inversion to beam profiles is non-singular for the entire configuration of beams. The natural Hilbert space metric among beam profiles provides another criterion for choosing beam angles. The total squared intensity at each beam angle (in the ρ - metric) is used as a ranking of beam orientations to maximize the overlap between the sampled and continuous beam profile functions. The measure is displayed relative to 3D tissue space contours to define an optimum subset of beams. The formalism is applied to real brain and prostate tumor data consisting of radiologist-generated tumor and organ-at-risk contours, prescribed dose and dose limits, and CT images.

Contrast agents with distinctive absorption and emission spectra, in combination with multispectral near-IR imaging, may provide a mechanism for the detection of breast cancer. While there is evidence of preferential drug accumulation at a tumor site, an important question is the concentration required to allow discrimination through tissue. An estimate of agent absorption effects is obtained from the solution of the diffusion equation in homogeneous tissue. In this paper absorption signatures derived from the diffusion equation and Monte Carlo simulation of a near-IR contrast agent, indocyanine green, are compared. Tradeoff curves are generated among the key relevant parameters; contrast, depth, and agent concentration. It is also shown that the diffusion equation solution for a localized contrast agent leads to an algorithm to estimate tumor location and depth from near-IR images. The algorithm is applied to in-vitro IR measurements of a tissue sample with an injected contrast agent. The results have application to the design of contrast enhancing drugs and associated discrimination algorithms.

Near-IR transillumination for breast cancer detection is limited by poor discrimination among different breast conditions; including the presense of cancerous, benign, and fibrous tissues. Perhaps due to enhanced vascularity, there is evidence that a contrast agent, indocyanine green (ICG), accumulates at the site of malignant tumors. The 805nm absorption edge of the compound suggests that multispectral transillumination, coupled with special-purpose data fusion processing, could enhance the discrimination of tumors. In this paper we report on the transillumination of in-vitro tissue samples in which minute (< 10 mu gm) quantities of ICG have been injected. Discrimination and deblurring algorithms fusing multispectral images in the range 750nm - 1000nm are applied to enhance detectability of the ICG in tissue.