Robert Y. Levine

Status of atmospheric correction using a MODTRAN4-based algorithm

The present disclosure is directed to maintaining horizontal alignment of peeling and cleaning rolls in a shrimp peeler and cleaner by restricting rotary movement of the posts without inhibiting up and down movement of adjacent posts at the base of which are journalled the peeling rolls. This is accomplished by molding flat walled projections at the base of the posts over which are received a locking member having a plurality of openings therethrough the walls of which are complemental to the flat walls of the projections in one direction and which are slightly greater in the other direction to permit relative vertical movement between adjacent posts.

Optimum beam configurations in tomographic intensity modulated radiation therapy

We review and extend the theory of tomographic dose reconstruction for intensity modulated radiotherapy (IMRT). We derive the basis for a saturation with beam number of dose conformation, and provide an analysis which ranks particular beam orientations in terms of the contribution to the delivered dose. Preferred beam directions are found which effectively reduce the number of beams necessary to achieve a given level of dose conformation. The analysis is a new application of the tomographic projection-slice theorem to the problem of beam orientation determination. The effects of the beam front filter and the positivity constraint arising from the tomographic approach are analysed, and modifications of the beam front filter for small beam numbers are suggested. The theory is applied to simple geometric shapes in two dimensions. A Gaussian ellipse, where analytical results are obtained, and simple hard-edged convex prescribed dose shapes are examined to illustrate beam selection based on the beam overlap metric. More complex concave prescribed dose shapes which contain a sensitive organ are also analysed and for low beam numbers are found to have preferred beam directions.

Validation of the QUick atmospheric correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery

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 new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi- and hyperspectral imaging sensors: QUAC (QUick atmospheric correction)

Abstract : We describe a new 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 realtime applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. In this paper, QUAC is applied to atmospherically correction several AVIRIS data sets. Comparisons to the physics-based FLAASH code are also presented.

Shadow-insensitive material detection/classification with atmospherically corrected hyperspectral imagery

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.

Automated optimal channel selection for spectral imaging sensors

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.

Bidirectional reflectance distribution function effects in ladar-based reflection tomography

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.

Three-dimensional tomographic reconstruction of an absorptive perturbation with diffuse photon density waves.

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.

Detection of cirrus clouds at 1.13 μm in AVIRIS scenes over land

Scattered solar radiance from cirrus clouds has traditionally been detected over land at 1.37 micrometer, 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 micrometer 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 micrometer and 1.37 micrometer 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 micrometer signal is closely related to the cloud optical thickness and can be used by itself or in combination with the 1.37 micrometer signal to correct apparent surface reflectance spectra for cirrus cloud effects.

Surface identification from multiband LADAR reflectance with varied incidence angle via database mapping.

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.