A Bayesian approach to identfication of gaseous effluents in passive LWIR imagery

Typically a regression approach is applied in order to identify the gaseous constituents present in a hyperspectral image, and the task of species identification amounts to choosing the best regression model. Common model selection approaches (stepwise and criterion based methods) have well known multiple comparisons problems, and they do not allow the user to control the experiment-wise error rate, or allow the user to include scene-specific knowledge in the inference process. A Bayesian model selection technique called Gibbs Variable Selection (GVS) that better handles these issues is presented and implemented via Markov chain monte carlo (MCMC). GVS can be used to simultaneously conduct inference on the optical path depth and probability of inclusion in a pixel for a each species in a library. This method flexibly accommodates an analyst\u27s prior knowledge of the species present in a scene, as well as mixtures of species of any arbitrary complexity. A modified version of GVS with fast convergence properties that is tailored to unsupervised use in hyperspectral image analysis will be presented. Additionally a series of automated diagnostic measures have been developed to monitor convergence of the MCMC with minimal operator intervention. Finally, the applicability of aggregating inference from adjacent pixels will be discussed. This method is compared against stepwise regression for model selection and results from LWIR data from the Airborne Hyperspectral Imager (AHI) are presented. Finally, the applicability of this method to operational scenarios and various sensors will be discussed

Modeling Spectral–Temporal Data From Point Source Events

In recent years, a great deal of effort has been invested in developing sensors to detect, locate, and identify “energetic” electromagnetic events. When observed through one type of imaging spectrometer, these events produce a data record that contains complete spectral and temporal information over the event’s evolution. This article describes the development of a statistical model for the data produced by a particular spectral–temporal sensor. While the application is unique in some ways, this approach to model building may be useful in other related contexts. Several plots, estimated parameters, and some additional details for an equation are provided in the Appendix which is available as supplementary material online.This article is published as "Modeling spectral-temporal data from point source events." Technometrics, 2011. Vol. 53, No. 2, pp. 183-195, DOI:10.1198/TECH.2011.09014. With Monica Reising, Max Morris, and Shawn Higbee.</p