Method and system for detecting targets known up to a simplex from multi-spectral and hyper-spectral imagery employing the normal compositional model

A computer program product, comprising;

• a computer readable medium having computer readable program code means embodied thereon for detecting anomalies in spectral data, said computer readable program code means including:
• a) first computer readable program means for receiving spectral data;
• b) second computer readable program means for using a normal compositional model for estimating background parameters from said spectral data and target components;
• c) third computer readable program means for estimating abundance values of classes of said normal compositional model from said background parameters and said spectral data;
• d) fourth computer readable program means for estimating target class covariance values from said spectral data, said background parameters, and said target components;
• e) fifth computer readable program means for estimating target-plus-background abundance values from said target class covariance values, said background parameters, spectral data, and said target components;
• f) sixth computer readable program means for employing a normal compositional model for determining a likelihood ratio detection statistic from said target class covariance values, said target-plus-background abundance values, said spectral data, target components, said background parameters, and background abundance values;
• and
• g) seventh computer readable program means for generating a determination output signal that represents whether an observation includes a target from said likelihood ratio detection statistic.

A system for detecting targets, comprising:

• a computer for executing a sequence of computer readable instructions for performing the processes of:
• a) receiving spectral data;
• b) using a normal compositional model for estimating background parameters from said spectral data and target components;
• c) estimating abundance values of classes of said normal compositional model from said background parameters and said spectral data;
• d) estimating target class covariance values from said spectral data, said background parameters, and said target components;
• e) estimating target-plus-background abundance values from said target class covariance values, said background parameters, spectral data, and said target components;
• f) employing a normal compositional model for determining a likelihood ratio detection statistic from said target class covariance values, said target-plus-background abundance values, said spectral data, said target components, said background parameters, and background abundance values;
• and
• g) generating a determination output signal that represents whether an observation includes a target from said likelihood ratio detection statistic.