**Publications**(55)

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Anomalous change detection is a two step process. Two co-registered images of a scene are first transformed to maximize the overall correlation between the images, then an anomalous change detector (ACD) is applied to the transformed images. The transforms maximize the correlation between the two images to attenuate the environmental differences that distract from the anomalous changes of importance.

Several categories of transforms with different optimization parameters are discussed and compared. One of two types of ACDs are then applied to the transformed images. The first ACD uses the difference of the two transformed images. The second concatenates the spectra of two images and uses an aggregated ACD. A comparison of the two ACD methods and their effectiveness with the different transforms is done for the first time.

^{TM}5 were used to generate coefficients for linear transformations used in the atmospheric transmission and compensation components of a typical end-to-end model. Model radiance statistics, calculated using reflectance data, is found to be similar to the original AVIRIS radiance data. Moreover, if identical atmospheric conditions are applied in the atmospheric transmission and in the atmospheric compensation model components and the effects of sensor noise are disregarded, the resulting reflectance statistics are identical to the original reflectance statistics.

*t*-elliptically contoured distribution. Traditionally, hyperspectral backgrounds have been modeled using multivariate Gaussian distributions; however it is well known that real data often exhibit "long-tail" behavior that cannot be accounted by normal distribution models. The proposed multivariate

*t*-distribution model has elliptical equiprobability contours whose center and ellipticity is specified by the mean vector and covariance matrix of the data. The density of the contours, which is reflected into the distribution of the Mahalanobis distance, is controlled by an extra parameter, the number of degrees of freedom. As the number of degrees of freedom increases, the tails decrease and approach those of a normal distribution with the same mean and covariance. In this work we investigate the application of

*t*-elliptically contoured distributions to the characterization of different hyperspectral background data obtained by visually interactive spatial segmentation ("physically" homogeneous classes), automated clustering algorithms using spectral similarity metrics (spectrally homogeneous classes), and by fitting normal mixture models (statistically homogeneous classes). These investigations are done using hyperspectral data from the AVIRIS sensor.

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