Statistical Modeling of Clutter in Hyperspectral Data using 3 D Markov Random Fields

Hyperspectral imagery provides high spectral and spatial resolution that can be discriminate between object and natural clutter in environmental monitoring applications such as coastal environment and coral reef monitoring. High dimensionality of the data set makes it difficult to apply statistical models to the full image. This papers presents a 3D Markov Random Field model that under the assumption of near spatial stationarity and a using the nearest spectral and spatial neighbors results in a statistical model that is parametrized only by four parameters.