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

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.

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Lunar Calibration for ASTER VNIR and TIR with Observations of the Moon in 2003 and 2017

Remote Sensing ◽

10.3390/rs11222712 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2712 ◽

Cited By ~ 3

Author(s):

Kouyama ◽

Kato ◽

Kikuchi ◽

Sakuma ◽

Miura ◽

...

Keyword(s):

Temporal Variation ◽

Near Infrared ◽

Thermal Emission ◽

Thermal Infrared ◽

Calibration Method ◽

The Other ◽

Radiometric Calibration ◽

The Moon ◽

Calibration Methods ◽

Better Than

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which is a multiband pushbroom sensor suite onboard Terra, has successfully provided valuable multiband images for approximately 20 years since Terra’s launch in 1999. Since the launch, sensitivity degradations in ASTER’s visible and near infrared (VNIR) and thermal infrared (TIR) bands have been monitored and corrected with various calibration methods. However, a unignorable discrepancy between different calibration methods has been confirmed for the VNIR bands that should be assessed with another reliable calibration method. In April 2003 and August 2017, ASTER observed the Moon (and deepspace) for conducting a radiometric calibration (called as lunar calibration), which can measure the temporal variation in the sensor sensitivity of the VNIR bands enough accurately (better than 1%). From the lunar calibration, 3–6% sensitivity degradations were confirmed in the VNIR bands from 2003 to 2017. Since the measured degradations from the other methods showed different trends from the lunar calibration, the lunar calibration suggests a further improvement is needed for the VNIR calibration. Sensitivity degradations in the TIR bands were also confirmed by monitoring the variation in the number of saturated pixels, which were qualitatively consistent with the onboard and vicarious calibrations.

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Spatial-Spectral-Emissivity Land-Cover Classification Fusing Visible and Thermal Infrared Hyperspectral Imagery

Remote Sensing ◽

10.3390/rs9090910 ◽

2017 ◽

Vol 9 (9) ◽

pp. 910 ◽

Cited By ~ 8

Author(s):

Yanfei Zhong ◽

Tianyi Jia ◽

Ji Zhao ◽

Xinyu Wang ◽

Shuying Jin

Keyword(s):

Land Cover ◽

Hyperspectral Imagery ◽

Thermal Infrared ◽

Land Cover Classification ◽

Spectral Emissivity

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Geological mapping of the Francistown area in northeastern Botswana by surface temperature and spectral emissivity information derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared data

Ore Geology Reviews ◽

10.1016/j.oregeorev.2013.01.005 ◽

2013 ◽

Vol 53 ◽

pp. 134-144 ◽

Cited By ~ 21

Author(s):

Taro Yajima ◽

Yasushi Yamaguchi

Keyword(s):

Surface Temperature ◽

Thermal Emission ◽

Thermal Infrared ◽

Spectral Emissivity ◽

Geological Mapping ◽

Infrared Data

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Anomaly Detection in Airborne Fourier Transform Thermal Infrared Spectrometer Images Based on Emissivity and a Segmented Low-Rank Prior

Remote Sensing ◽

10.3390/rs13040754 ◽

2021 ◽

Vol 13 (4) ◽

pp. 754

Author(s):

Xuhe Zhu ◽

Liqin Cao ◽

Shaoyu Wang ◽

Lyuzhou Gao ◽

Yanfei Zhong

Keyword(s):

Fourier Transform ◽

Anomaly Detection ◽

Mahalanobis Distance ◽

Hyperspectral Imagery ◽

Matrix Decomposition ◽

Thermal Infrared ◽

Low Rank ◽

Background Information ◽

Spectral Difference ◽

Infrared Spectrometer

Although hyperspectral anomaly detection is commonly conducted in the visible, near-infrared, and shortwave infrared spectral regions, there has been less research on hyperspectral anomaly detection in the longwave infrared (LWIR) hyperspectral region. The radiance of thermal infrared hyperspectral imagery is determined by the temperature and emissivity. To avoid the detection uncertainty caused by the single factor of temperature, emissivity can be introduced to detect anomalies. However, in the emissivity domain, the spectral contrast and signal-to-noise ratio (SNR) are low, which makes it difficult to separate the anomalies from the background. In this paper, an anomaly detection method combining emissivity and a segmented low-rank prior (EaSLRP) is proposed for use with thermal infrared hyperspectral imagery. The EaSLRP method is divided into three parts—1) temperature/emissivity retrieval, 2) extraction of the thermal infrared hyperspectral background information, and 3) Mahalanobis distance detection. A homogeneous region generation method is also proposed to solve the problem of the complex global background leading to inaccurate background estimation. The GoDec method is used for matrix decomposition and background information extraction and to remove some of the noise. The proposed Mahalanobis distance detector then uses the background component and original image for anomaly detection, while highlighting the spectral difference between the anomalies and background. This method can also suppress the influence of noise, to some extent. The experimental results obtained with airborne Fourier transform thermal infrared spectrometer hyperspectral images demonstrate that the EaSLRP method is effective when compared with the Reed–Xiaoli detector (RXD), the segmented RX detector (SegRX), the low-rank and sparse representation-based detector (LRASR), the low-rank and sparse matrix decomposition (LRaSMD)-based Mahalanobis distance method (LSMAD), and the locally enhanced low-rank prior method (LELRP-AD).

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Faculty Opinions recommendation of G-matrix Fourier transform NOESY-based protocol for high-quality protein structure determination.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1026549.323382 ◽

2005 ◽

Author(s):

Deyou Zheng

Keyword(s):

Fourier Transform ◽

Protein Structure ◽

Structure Determination ◽

Protein Structure Determination ◽

High Quality ◽

High Quality Protein

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Fourier transform spectrometer for spectral emissivity measurement in the temperature range between 60 and 1500°C

Journal of Physics Conference Series ◽

10.1088/1742-6596/13/1/015 ◽

2005 ◽

Vol 13 ◽

pp. 63-66 ◽

Cited By ~ 15

Author(s):

Jingmin Dai ◽

Xinbei Wang ◽

Guibin Yuan

Keyword(s):

Fourier Transform ◽

Spectral Emissivity ◽

Fourier Transform Spectrometer ◽

Temperature Range ◽

Emissivity Measurement

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Implementation of continuous fast scanning detection in femtosecond Fourier-transform two-dimensional vibrational-electronic spectroscopy to decrease data acquisition time

Review of Scientific Instruments ◽

10.1063/1.5048523 ◽

2018 ◽

Vol 89 (11) ◽

pp. 113104 ◽

Cited By ~ 4

Author(s):

Zachary W. Fox ◽

Tyler J. Blair ◽

Robert B. Weakly ◽

Trevor L. Courtney ◽

Munira Khalil

Keyword(s):

Fourier Transform ◽

Data Acquisition ◽

Electronic Spectroscopy ◽

Acquisition Time ◽

Two Dimensional ◽

Data Acquisition Time ◽

Scanning Detection

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Compact calibration source for thermal infrared Fourier-transform spectrometer

Infrared Remote Sensing and Instrumentation XXVII ◽

10.1117/12.2528692 ◽

2019 ◽

Author(s):

Alexey Shakun ◽

Fedor Martynovich ◽

Nikolay Ignatiev ◽

Alessandro Maturilli ◽

Aleksandr Santos-Skripko ◽

...

Keyword(s):

Fourier Transform ◽

Thermal Infrared ◽

Fourier Transform Spectrometer ◽

Calibration Source

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A Study on Pattern Recognition with the Histograms of Oriented Gradients in Distorted and Noisy Images

JUCS - Journal of Universal Computer Science ◽

10.3897/jucs.2020.024 ◽

2020 ◽

Vol 26 (4) ◽

pp. 454-478

Author(s):

Andrzej Bukała ◽

Michał Koziarski ◽

Bogusław Cyganek ◽

Osman Koç ◽

Alperen Kara

Keyword(s):

Pattern Recognition ◽

Negative Impact ◽

Quality Data ◽

High Quality ◽

Denoising Method ◽

High Quality Data ◽

Histograms Of Oriented Gradients ◽

Different Types ◽

Simple Implementation ◽

Salt And Pepper

Histograms of oriented gradients (HOG) are still one of the most frequently used low-level features for pattern recognition in images. Despite their great popularity and simple implementation performance of the HOG features almost always has been measured on relatively high quality data which are far from real conditions. To fill this gap we experimentally evaluate their performance in the more realistic conditions, based on images affected by different types of noise, such as Gaussian, quantization, and salt-and-pepper, as well on images distorted by occlusions. Different noise scenarios were tested such anti-distortions during training as well as application of a proper denoising method in the recognition stage. As underpinned with experimental results, the negative impact of distortions and noise on object recognition with HOG features can be significantly reduced by employment of a proper denoising strategy.

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