Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging

Ramzi Idoughi,Marc-André Gagnon,Thomas H G Vidal,Pierre-Yves Foucher,Xavier Briottet

doi:10.1155/2016/5428762

Abstract

Hyperspectral imaging in the long-wave infrared (LWIR) is a mean that is proving its worth in the characterization of gaseous effluent. Indeed the spectral and spatial resolution of acquisition instruments is steadily decreasing, making the gases characterization increasingly easy in the LWIR domain. The majority of literature algorithms exploit the plume contribution to the radiance corresponding to the difference of radiance between the plume-present and plume-absent pixels. Nevertheless, the off-plume radiance is unobservable using a single image. In this paper, we propose a new method to retrieve trace gas concentration from airborne infrared hyperspectral data. More particularly the outlined method improves the existing background radiance estimation approach to deal with heterogeneous scenes corresponding to industrial scenes. It consists in performing a classification of the scene and then applying a principal components analysis based method to estimate the background radiance on each cluster stemming from the classification. In order to determine the contribution of the classification to the background radiance estimation, we compared the two approaches on synthetic data and Telops Fourier Transform Spectrometer (FTS) Imaging Hyper-Cam LW airborne acquisition above ethylene release. We finally show ethylene retrieved concentration map and estimate flow rate of the ethylene release.

Highlights

Anthropogenic sources, especially industrial, have a major contribution to air pollution and security issues
Signature of gaseous effluents differs highly from usual targets since the plume modifies the spectral signature of the background: different pixels in the data cube that contain the same gaseous plume could have a totally different spectral signature
In order to improve the background radiance estimation given by the Selected-Band approach and the Clustering-Based method, we propose to perform this method on each cluster stemming from the classification (Section 2.3.1)

Summary

Introduction

Anthropogenic sources, especially industrial, have a major contribution to air pollution and security issues These emissions remain poorly estimated at a high spatial resolution over heterogeneous scenes, like industrial plants. Most of these emissions present a spectral signature in the thermal infrared domain. Spectral signature of these pixels will correspond to background spectra affected by either absorption or emission of the gas. More the outlined method improves the existing background radiance estimation approach to deal with heterogeneous scenes corresponding to industrial scenes. In order to determine the contribution of the classification to the background radiance estimation, we compared the two approaches on synthetic data and Telops Fourier Transform Spectrometer (FTS) Imaging Hyper-Cam LW airborne acquisition above ethylene release. We show ethylene retrieved concentration map and estimate flow rate of the ethylene release

Objectives

Results

Conclusion