New joint estimation method for emissivity and temperature distribution based on a Kriged Marginalized Particle Filter: Application to simulated infrared thermal image sequences

Abstract

This paper addresses the challenge of simultaneously estimating temperature and emissivity for infrared thermography in natural environment, aiming for near real-time performance. Existing methods, mainly in satellite observation field, rely on restrictive physical assumptions unsuitable for ground-based application context (Structures and Infrastructures monitoring). Other generic methods are nonetheless computationally intensive, making them impractical for real-time use. Our objective is to provide a method with effective real-time calculation performance while still giving results comparable to those reference methods under the same hypotheses, finally achieving both good accuracy and performance. The proposed method is based on a dynamical state-space modeling for the temperature, where the state vector is assumed to be split into a dynamic component for the temperature and a stationary component representing the emissivity. Then the dynamical component is estimated by a Kalman filter approach, whereas the parameterized model and the emissivity component are estimated through a particle filtering framework resulting in a bank of Kalman filters, also called marginalized particle filter. A spatial assumption of homogeneity for the temperature yields to the addition of a Kriging step to the Marginalized Particle Filter to overcome the ill-posed nature of the problem and to compute the necessary physical estimates in a reasonable amount of time while providing fair results compared to reference methods from the literature.

A comparison with two state-of-the-art methods, MCMC and CMA-ES, is presented. The results indicate that the proposed method estimates the true value within a maximum deviation of $3\text{K}$, similar to CMA-ES, while MCMC achieves a more accurate estimate with a maximum deviation of $0.5\text{K}$. However, the computational efficiency of the proposed method is significantly improved, reducing the processing time by seven orders of magnitude compared to MCMC and three orders of magnitude compared to CMA-ES. This remarkable efficiency highlights the method’s feasibility for real-time monitoring of temperature and emissivity.