Field-Based High-Quality Emissivity Spectra Measurement Using a Fourier Transform Thermal Infrared Hyperspectral Imager

Abstract

Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spectral emissivity measurement standards for TIR hyperspectral imagers in the field. In this paper, we address the problems encountered when measuring emissivity spectra in the field and propose a practical data acquisition and processing framework for a Fourier transform (FT) TIR hyperspectral imager—the Hyper-Cam LW—to obtain high-quality emissivity spectra in the field. This framework consists of three main parts. (1) The performance of the Hyper-Cam LW sensor was evaluated in terms of the radiometric calibration and measurement noise, and a data acquisition procedure was carried out to obtain the useful TIR hyperspectral imagery in the field. (2) The data quality of the original TIR hyperspectral imagery was improved through preprocessing operations, including band selection, denoising, and background radiance correction. A spatial denoising method was also introduced to preserve the atmospheric radiance features in the spectra. (3) Three representative temperature-emissivity separation (TES) algorithms were evaluated and compared based on the Hyper-Cam LW TIR hyperspectral imagery, and the optimal TES algorithm was adopted to determine the final spectral emissivity. These algorithms are the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm, the improved Advanced Spaceborne Thermal Emission and Reflection Radiometer temperature and emissivity separation (ASTER-TES) algorithm, and the Fast Line-of-sight Atmospheric Analysis of Hypercubes-IR (FLAASH-IR) algorithm. The emissivity results from these different methods were compared to the reference spectra measured by a Model 102F spectrometer. The experimental results indicated that the retrieved emissivity spectra from the ISSTES algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer. The root-mean-square error (RMSE) between the retrieved emissivity and the standard spectra was 0.0086, and the spectral angle error was 0.0093.

Dual-Interference Channels Static Fourier Transform Imaging Spectrometer Based on Stepped Micro-Mirror: Data Processing and Experiment Research

“…The specific conditions of the segmented concentration quantitative correction model of each gas are shown in The total multi-concentration calibration set of each gas is divided into four concentration calibration sub-sets according to the transmittance threshold of the spectral characteristic wavenumber points, and the transmittance and concentration of each spectral point in the sub-sets are modeled using partial least squares regression, and the concentration prediction model of each gas is established. R 2 is used as the model accuracy evaluation function, as shown in Equation (29). The closer the value is to 1, the more accurate the model prediction is.…”

“…In 2019, Qiwei et al proposed an interferogram difference algorithm to suppress channel crosstalk based on a polarization-difference channeled imaging spectropolarimeter using a double Wollaston prism, which reduces spectral and polarization characteristic errors [28]. In 2021, Lyzhou Gao et al proposed a high-quality emissivity spectral measurement algorithm based on the Fourier transform thermal infrared hyperspectral imager, which significantly improved the accuracy of the reconstructed spectrum [29]. In the same year, Walid ElSayed ElZeiny et al proposed a spectral reconstruction algorithm based on the position relationship between the detector signal and the scanning mirror, which reduced the spectral reconstruction error [30].…”

Quantitative Analysis of Mixed Minerals with Finite Phase Using Thermal Infrared Hyperspectral Technology

“…The method of obtaining sample emissivity in this study refers to the study [ 33 ]. Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm is widely used in remote sensing applications to accurately calculate the emissivity of solid objects from their spectral radiance measurements.…”