Translator Disclaimer
17 May 2016 Temperature-emissivity separation for LWIR sensing using MCMC
Author Affiliations +
Signal processing for long-wave infrared (LWIR) sensing is made complicated by unknown surface temperatures in a scene which impact measured radiance through temperature-dependent black-body radiation of in-scene objects. The unknown radiation levels give rise to the temperature-emissivity separation (TES) problem describing the intrinsic ambiguity between an object’s temperature and emissivity. In this paper we present a novel Bayesian TES algorithm that produces a probabilistic posterior estimate of a material’s unknown temperature and emissivity. The statistical uncertainty characterization provided by the algorithm is important for subsequent signal processing tasks such as classification and sensor fusion. The algorithm is based on Markov chain Monte Carlo (MCMC) methods and exploits conditional linearity to achieve efficient block-wise Gibbs sampling for rapid inference. In contrast to existing work, the algorithm optimally incorporates prior knowledge about inscene materials via Bayesian priors which may optionally be learned using training data and a material database. Examples demonstrate up to an order of magnitude reduction in error compared to classical filter-based TES methods.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joshua N. Ash and Joseph Meola "Temperature-emissivity separation for LWIR sensing using MCMC", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98401O (17 May 2016);

Back to Top