Earth observing satellites usually not only take ordinary red-green-blue images, but provide several images including the near-infrared and infrared spectrum. These images are called multispectral, for about four to seven different bands, or hyperspectral, for higher dimensional images of up to 210 bands. The drawback of the additional spectral information… (More)
—The goal of pan-sharpening is to fuse a low spatial resolution multispectral image with a higher resolution panchro-matic image to obtain an image with high spectral and spatial resolution. The Intensity-Hue-Saturation (IHS) method is a popular pan-sharpening method used for its efficiency and high spatial resolution. However, the final image produced… (More)
Given discrete event data, we wish to produce a probability density that can model the relative probability of events occurring in a spatial region. Common methods of density estimation, such as Kernel Density Estimation, do not incorporate geographical information. Using these methods could result in non-negligible portions of the support of the density in… (More)
Although hyperspectral images contain much more information than regular RGB images, more than ninety percent of the variance can be explained by a small portion of the data. The goal of dimension reduction is to map high dimensional data into a lower dimension while preserving the main features of the original data. The dimension reduction codes are taken… (More)
Recent research in perinatal pathology argues that analyzing properties of the placenta may reveal important information on how certain diseases progress. One important property is the structure of the placental blood vessels, which supply a fetus with all of its oxygen and nutrition. An essential step in the analysis of the vascular network pattern is the… (More)
In this paper, we extend the Chan-Vese model for image segmentation in  to hyperspectral image segmentation with shape and signal priors. The use of the Split Bregman algorithm makes our method very efficient compared to other existing segmentation methods incorporating priors. We demonstrate our results on aerial hyperspectral images.
Given discrete event data, we wish to produce a probability density that can model the relative probability of events occurring in a spatial region. Common methods of density estimation, such as Kernel Density Estimation, do not incorporate geographical information. Using these methods could result in nonnegligible portions of the support of the density in… (More)
Understanding the correlation between stocks may give us new insight into modeling the behavior of financial markets. Multivariate stochastic volatility models can be used to describe the time-varying correlation between asset returns. We propose a new multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model which incorporates… (More)