ML detection in hyperspectral imagery: a GMRF modeling approach

The use of hyperspectral imagery for remote sensing detection applications has received attention recently due to the ability of the hyperspectral sensor to provide registered information in both space and frequency. In this paper we extend our work on the development of an efficient implementation of the Maximum Likelihood (ML) detector in which we use a 3D Gauss-Markov random field to model the clutter background in the hyperspectral data. We review the details of the optimal ML estimation approach for obtaining the Markov parameters and discuss a gradient based optimization scheme for obtaining these estimates. To improve the computational efficiency of the overall detection algorithm, we develop an estimation method based on some simple mathematical approximations that allows us to explicitly solve for the Markov parameters. In addition, we use a stochastic, rather than deterministic target model by implementing a single hypothesis test in place of the more traditional binary hypothesis paradigm. We compare the detection performance and the computational requirements of our updated model and detector implementation to the benchmark RX detection algorithm for hyperspectral imagery.