The Johnson System for characterizing an empirical distribution is used to model the non-normal behavior of
Mahalanobis distances in material clusters extracted from hyperspectral imagery data. An automated method for
determining Johnson distribution parameters is used to model Mahalanobis distance distributions and is compared to an
existing method which uses mixtures of F distributions. The results lead to a method for determining outliers and
mitigating their effects.
Proc. SPIE. 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII
KEYWORDS: Statistical analysis, Detection and tracking algorithms, Data modeling, Image processing, Interference (communication), Tomography, Data processing, Signal processing, Multidimensional signal processing, Expectation maximization algorithms
Many imaging techniques commonly involve the extraction of mixed signal information from a pixel. In most mixed pixel cases, this is assumed to be a linear mixture and signal separation routines have been developed with this mixing compositions scheme in mind. One such signal separation routine incorporates the Expectation Maximization Maximum Likelihood (EMML) algorithm for the determination of signal mixtures in a pixel. This routine, however is very inefficient in that it requires large iteration values to converge to a solution. This report is the result of the implementation of a Re-scaled Block Iterative EMML approach, commonly used in the medical field for emission tomography image processing, to perform signal separation, while greatly increasing the efficiency in computation and rate of convergence to a solution.
Image pixels represent either distinct materials (end members) that are present in the image, or mistures of two or more of these pure materials. Estimates of pure end member spectra are needed for spectral libraries and for input into pixel unmixing codes. We investigate three algorithms for estimating end member spectra: (1) the convex hull method in which an n-dimensional surface is shrink- wrapped around the data cloud; (2) a pixel-by-pixel search method in which pixels that have sufficiently different spectral angles are declared end members; (3) a pixel-by- pixel search method using Euclidean distance as a measure, followed by clustering to improve the estimate of the spectra. The convex hull technique should provide an estimate of pure end member spectra while the pixel-by-pixel search methods should find both distinct end members and distinct mixtures. Each method requires user-set thresholds to find distinct spectra, which can be expressed in spectral angle degrees or image-dependent units for Euclidean distance. Estimates for the lower threshold (below which two spectra are considered to be the same material) and the upper threshold (above which two spectra are definitely different materials) are derived empirically. Low-altitude AVIRIS data will be used to demonstrate the utility of these end member extraction methods. We will illusxtrate how well each technique compare to the other, and compare how well individual algorithms work across adjacent scenes.
Atmospheric emission, scattering, and photon absorption degrade spectral imagery data and reduce its utility. The Air Force Research Laboratory and Spectral Sciences, Inc. are developing a MODTRAN4-based 'atmospheric mitigation’ algorithm to support current and planned IR-visible-UV sensor spectral radiance imagery measurements. The intent is to provide surface reflectance and emissivity imagery data of sufficient accuracy for input into subsequent analyses of surface properties, effectively removing the atmospheric component. This report is the result of the application of the atmospheric mitigation algorithm to a NASA/JPL AVIRIS spectral image cube as a pre-processing step towards improving the performance of image categorization routines.
Atmospheric emission, scattering, and photon absorption degrade spectral imagery data and reduce its utility. We report on the use of an atmospheric compensation code for the visible and near-infrared, based on MODTRAN 4, that includes spectral analysis, accounts for interference to a given pixel by adjacent pixels, and provides a polishing routine to clear residual atmospheric spectral features common to a group of pixels. A NASA/JPL AVIRIS data sample is analyzed.
Laser radar images of an outdoor target scene were collected in adverse weather such as rain and fog during the course of one year. Included in this collection is imagery in fogs with visibilities less than 2 km and rains with rain rates of up to 60 mm/hr. The targets were calibrated panels at 510 m and 1 km. The laser radar system used was a direct- detection 1.06 micrometers system designed to operate at 2 km in clear weather. For the purposes described here, though, the maximum range gate was set to 1.5 km. The system used a correlation technique for detection and discrimination, which significantly reduced the number of false returns in fog. Using these collected images, dropout pixels and false returns were correlated with rain rate and visibility. Extinction coefficients for 1.064 micrometers laser light were also calculated as a function of rain rate and visibility in fog and rain conditions. These coefficients were found to be consistent with those measured previously at 0.55 micrometers , 0.63 micrometers and 10.6 micrometers . These coefficients can be used to predict the performance of any circular polarized 1.064 micrometers LADAR system in rain and fog conditions.
Laser radar image of an outdoor target scene were collected in adverse weather such as rain and fog during the course of one year. Included in this collection is imagery in fogs with visibilities less than 0.2 km and rains with rain rates of up to 180 mm/hr. The targets were small buildings, target panels and a mobile target, all approximately 500 m in distance from the laser radar system. The laser radar system used was a direct-detection 1.06 micrometers system designed to operate at 1 km in clear weather. Using these collected images, dropout pixels and false returns were correlated with rain rate and visibility. Dropouts and false returns were found to follow a linear relationship with rain rate and an exponential decreasing relationship with visibility. Empirical equations were developed from least square fits of the data to predict the dropouts and false returns, given the rain rate and visibility. Finally, fog and rain data from 450 images was combined and correlated into visibility intervals so that one can predict the dropout and false return percentages given a visibility in either fig or rain.