Статті в журналах: "Thermal hyperspectral imaging"

1

Boubanga-Tombet, Stephane, Alexandrine Huot, Iwan Vitins, Stefan Heuberger, Christophe Veuve, Andreas Eisele, Rob Hewson, Eric Guyot, Frédérick Marcotte, and Martin Chamberland. "Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face." Remote Sensing 10, no. 10 (September 21, 2018): 1518. http://dx.doi.org/10.3390/rs10101518.

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Remote sensing systems are largely used in geology for regional mapping of mineralogy and lithology mainly from airborne or spaceborne platforms. Earth observers such as Landsat, ASTER or SPOT are equipped with multispectral sensors, but suffer from relatively poor spectral resolution. By comparison, the existing airborne and spaceborne hyperspectral systems are capable of acquiring imagery from relatively narrow spectral bands, beneficial for detailed analysis of geological remote sensing data. However, for vertical exposures, those platforms are inadequate options since their poor spatial resolutions (metres to tens of metres) and NADIR viewing perspective are unsuitable for detailed field studies. Here, we have demonstrated that field-based approaches that incorporate thermal infrared hyperspectral technology with about a 40-nm bandwidth spectral resolution and tens of centimetres of spatial resolution allow for efficient mapping of the mineralogy and lithology of vertical cliff sections. We used the Telops lightweight and compact passive thermal infrared hyperspectral research instrument for field measurements in the Jura Cement carbonate quarry, Switzerland. The obtained hyperspectral data were analysed using temperature emissivity separation algorithms to isolate the different contributions of self-emission and reflection associated with different carbonate minerals. The mineralogical maps derived from measurements were found to be consistent with the expected carbonate results of the quarry mineralogy. Our proposed approach highlights the benefits of this type of field-based lightweight hyperspectral instruments for routine field applications such as in mining, engineering, forestry or archaeology.

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2

Marwaha, R., A. Kumar, P. L. N. Raju, and Y. V. N. Krishna Murthy. "Target detection algorithm for airborne thermal hyperspectral data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 827–32. http://dx.doi.org/10.5194/isprsarchives-xl-8-827-2014.

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Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm−1 to 1280 cm−1 region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm−1 (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.

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3

Jung, András, Péter Kardeván, and László Tőkei. "Hyperspectral Technology in Vegetation Analysis." Progress in Agricultural Engineering Sciences 2, no. 1 (December 1, 2006): 95–117. http://dx.doi.org/10.1556/progress.2.2006.1.5.

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The objective of the work reported is the development of red-edge methodology in order to characterize agricultural vegetation types and the determination of relationships between different vegetation (high biomass, low biomass) and thermal images. Therefore, the aim was to calculate red-edge position (REP) values and compare them to traditional vegetation indices (NDVI) and thermal images. Images were taken by a DAIS 7915 airborne imaging spectrometer that was equipped with an additional thermal imaging system. An exponential relationship was found between the on-curve-evaluation based (REP) and the broad band vegetation indices (NDVI). A linear relationship was determined between surface temperature differences ( ΔTs ) of the vegetation and NDVI values. A logarithmic relationship was found between surface temperature differences ( ΔTs ) of the vegetation of the canopy and red-edge position (REP). NDVI and REP are suitable vegetation indices when there are several bands available in the spectral range of 600-800 nm. REP was found to be a suitable method for analyzing and characterizing vegetated surfaces.

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4

Liu, Bingxin, Yulong Du, Chengyu Liu, and Ying Li. "A Practical Method for Blind Pixel Detection for the Push-Broom Thermal-Infrared Hyperspectral Imager." Sensors 22, no. 19 (September 29, 2022): 7403. http://dx.doi.org/10.3390/s22197403.

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Thermal infrared hyperspectral imager is one of the frontier payloads in current hyperspectral remote sensing research. It has broad application prospects in land and ocean temperature inversion, environmental monitoring, and other fields. However, due to the influence of the production process of the infrared focal plane array and the characteristics of the material itself, the infrared focal plane array inevitably has blind pixels, resulting in spectral distortion of the data or even invalid data, which limits the application of thermal infrared hyperspectral data. Most of the current blind pixels detection methods are based on the spatial dimension of the image, that is, processing single-band area images. The push-broom thermal infrared hyperspectral imager works completely different from the conventional area array thermal imager, and only one row of data is obtained per scan. Therefore, the current method cannot be directly applied to blind pixels detection of push-broom thermal infrared hyperspectral imagers. Based on the imaging principle of push-broom thermal infrared hyperspectral imager, we propose a practical blind pixels detection method. The method consists of two stages to detect and repair four common types of blind pixels: dead pixel, dark current pixel, blinking pixel, and noise pixel. In the first stage, dead pixels and dark current pixels with a low spectral response rate are detected by spectral filter detection; noise pixels are detected by spatial noise detection; and dark current pixels with a negative response slope are detected by response slope detection. In the second stage, according to the random appearance of blinking pixels, spectral filter detection is used to detect and repair spectral anomalies caused by blinking pixels line by line. In order to verify the effectiveness of the proposed method, a flight test was carried out, using the Airborne Thermal-infrared Hyperspectral Imaging System (ATHIS), the latest thermal infrared imager in China, for data acquisition. The results show that the method proposed in this paper can accurately detect and repair blind pixel, thus effectively eliminating spectral anomalies and significantly improving image quality.

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5

Aref, Mohamed Hisham, Ibrahim H. Aboughaleb, and Yasser H. El-Sharkawy. "Tissue characterization utilizing hyperspectral imaging for liver thermal ablation." Photodiagnosis and Photodynamic Therapy 31 (September 2020): 101899. http://dx.doi.org/10.1016/j.pdpdt.2020.101899.

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6

Schlerf, Martin, Gilles Rock, Philippe Lagueux, Franz Ronellenfitsch, Max Gerhards, Lucien Hoffmann, and Thomas Udelhoven. "A Hyperspectral Thermal Infrared Imaging Instrument for Natural Resources Applications." Remote Sensing 4, no. 12 (December 14, 2012): 3995–4009. http://dx.doi.org/10.3390/rs4123995.

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7

Diani, Marco, Matteo Moscadelli, and Giovanni Corsini. "Improved Alpha Residuals for Target Detection in Thermal Hyperspectral Imaging." IEEE Geoscience and Remote Sensing Letters 15, no. 5 (May 2018): 779–83. http://dx.doi.org/10.1109/lgrs.2018.2808372.

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8

Gorin, Brian A. "New method to optimize atmospheric correction for hyperspectral thermal imaging." Optical Engineering 41, no. 9 (September 1, 2002): 2088. http://dx.doi.org/10.1117/1.1499498.

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9

Öner, Bahar, James W. Pomeroy, and Martin Kuball. "Submicrometer Resolution Hyperspectral Quantum Rod Thermal Imaging of Microelectronic Devices." ACS Applied Electronic Materials 2, no. 1 (December 3, 2019): 93–102. http://dx.doi.org/10.1021/acsaelm.9b00575.

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10

Kastek, Mariusz, Krzysztof Firmanty, Benjamin Saute, Philippe Gagnon, Martin Lariviere-Bastien, and Daniel Pawelski. "Detection, Identification, and Quantification of SF6 Point-Source Emissions Using Hyper-Cam LW Airborne Platform." Pomiary Automatyka Robotyka 25, no. 3 (September 13, 2021): 37–41. http://dx.doi.org/10.14313/par_241/37.

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Detection, identification, and quantification of greenhouse gases is essential to ensure compliance with regulatory guidelines and mitigate damage associated with anthropogenic climate change. Passive infrared hyperspectral imaging technology is among the solutions that can detect, identify and quantify multiple greenhouse gases simultaneously. The Telops Hyper-Cam Airborne Platform is an established system for aerial thermal infrared hyperspectral measurements for gas survey applications. In support of the Hypercam, is developing a suite of hyperspectral imaging data processing algorithms that allow for gas detection, identification, and quantification in real-time. In the Fall of 2020, the Hyper-Cam-LW Airborne platform was flown above a validated SF6 gas release system to collect hyperspectral data for gas quantification analysis. This measurement campaign was performed to document performance of the Hyper-Cam gas quantification capabilities against known quantities of released gas.

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11

Hisham Aref, Mohamed, Ibrahim H. Aboughaleb, Mohamed Rabie, and Yasser H. El-Sharkawy. "Spatiotemporal Thermal Contours mapping of ex-vivo Bovine Liver Radiofrequency Thermal Ablation utilizing Hyperspectral Image and its Associated K-Mean Clustering Algorithm." Biomedical Research and Clinical Reviews 1, no. 1 (June 30, 2020): 01–13. http://dx.doi.org/10.31579/2692-9406/005.

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Significance: Hepatocellular carcinoma (HCC) is considered as worldwide health problem with a poor diagnosis due to limited detection techniques. Thermal ablation is the dominant modality to treat liver tumors for discriminating patients who are not allowed to have surgical intervention. Knowing that, observing or foreseeing the size of the subsequent tissue putrefaction during the Thermal Ablation techniques is a difficult undertaking. Aim: To examine the impacts of ablation zone volume following Radiofrequency ablation (RFA) of an ex-vivo bovine liver to correlate the impacts of thermal ablation with target organ perfusion; by exploiting the unique properties of Hyperspectral Imaging (HSI).where, Vessels may source cooling in the adjacent tumor target (heat‑sink‑effect) with risk of cancer recurrence and the infiltration profundity estimations consider the lessening of the tissue. Materials and Methods: Radiofrequency ablation was perfused on ex-vivo bovine livers at peripheral and central‑vessel‑adjacent locations, and monitored by HSI with a spectral range from 400 to 1000 nm. The system contains k-means clustering (K=8) algorithms combining spectral and spatial information. Labeled spectral signatures datasets were used as training data. Statistical analysis (10 samples) was computed to calculate the highest variance between six spectral images for determining the optimum wavelength for discrimination between the affected regions after thermal ablation (normal, thermal, and ablated liver tissue regions). Results: The change of the optical properties of ex-vivo liver tissues provides different responses to light transmission, scattering, absorption and particularly the reflection over the spectrum range. The spectral reflectance signatures were measured and evaluated using designed K-mean clustering algorithm after image reconstructed. Trials showed that spectral region 650~650 nm was proposed as optimum spectral range. Where, these results successfully distinguishes the Surface Thermal ablation region (x,y-axis),as well as the Thermal penetration Depth (z-axis) for Tissue characterization and Contour mapping for the unwanted thermal damage. Conclusions: Hyperspectral imaging is a powerful tool in real-time monitoring the thermal ablation and more accurate compared to the conventional imaging modality.

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12

Hou, Fujin, Yan Zhang, Yong Zhou, Mei Zhang, Bin Lv, and Jianqing Wu. "Review on Infrared Imaging Technology." Sustainability 14, no. 18 (September 6, 2022): 11161. http://dx.doi.org/10.3390/su141811161.

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The application of infrared camera-related technology is a trending research topic. By reviewing the development of infrared thermal imagers, this paper introduces several main processing technologies of infrared thermal imagers, expounds the image nonuniformity correction, noise removal, and image pseudo color enhancement of infrared thermal imagers, and briefly analyzes some main algorithms used in image processing. The technologies of blind element detection and compensation, temperature measurement, target detection, and tracking of infrared thermal imager are described. By analyzing the main algorithms of infrared temperature measurement, target detection, and tracking, the advantages and disadvantages of these technologies are put forward. At the same time, the development of multi/hyperspectral infrared remote sensing technology and its application are also introduced. The analysis shows that infrared thermal imager processing technology is widely used in many fields, especially in the direction of autonomous driving, and this review helps to expand the reader’s research ideas and research methods.

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13

Hanuš, J., T. Fabiánek, and L. Fajmon. "POTENTIAL OF AIRBORNE IMAGING SPECTROSCOPY AT CZECHGLOBE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 15–17. http://dx.doi.org/10.5194/isprsarchives-xli-b1-15-2016.

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Ecosystems, their services, structures and functions are affected by complex environmental processes, which are both natural and human-induced and globally changing. In order to understand how ecosystems behave in globally changing environment, it is important to monitor the current status of ecosystems and their structural and functional changes in time and space. An essential tool allowing monitoring of ecosystems is remote sensing (RS). Many ecosystems variables are being translated into a spectral response recorded by RS instruments. It is however important to understand the complexity and synergies of the key ecosystem variables influencing the reflected signal. This can be achieved by analysing high resolution RS data from multiple sources acquired simultaneously from the same platform. Such a system has been recently built at CzechGlobe - Global Change Research Institute (The Czech Academy of Sciences). CzechGlobe has been significantly extending its research infrastructure in the last years, which allows advanced monitoring of ecosystem changes at hierarchical levels spanning from molecules to entire ecosystems. One of the CzechGlobe components is a laboratory of imaging spectroscopy. The laboratory is now operating a new platform for advanced remote sensing observations called FLIS (Flying Laboratory of Imaging Spectroscopy). FLIS consists of an airborne carrier equipped with passive RS systems. The core instrument of FLIS is a hyperspectral imaging system provided by Itres Ltd. The hyperspectral system consists of three spectroradiometers (CASI 1500, SASI 600 and TASI 600) that cover the reflective spectral range from 380 to 2450 nm, as well as the thermal range from 8 to 11.5 μm. The airborne platform is prepared for mounting of full-waveform laser scanner Riegl-Q780 as well, however a laser scanner is not a permanent part of FLIS. In 2014 the installation of the hyperspectral scanners was completed and the first flights were carried out with all sensors. The new hyperspectral imaging system required adaptations in the data pre-processing chain. The established pre-processing chain (radiometric, atmospheric and geometric corrections), which was tailored mainly to the AISA Eagle instrument operated at CzechGlobe since 2004, has been now modified to fit the new system and users needs. Continuous development of the processing chain is now focused mainly on establishing pre-processing of thermal data including emissivity estimation and also on joint processing of hyperspectral and laser scanning data.

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14

Hanuš, J., T. Fabiánek, and L. Fajmon. "POTENTIAL OF AIRBORNE IMAGING SPECTROSCOPY AT CZECHGLOBE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 15–17. http://dx.doi.org/10.5194/isprs-archives-xli-b1-15-2016.

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Ecosystems, their services, structures and functions are affected by complex environmental processes, which are both natural and human-induced and globally changing. In order to understand how ecosystems behave in globally changing environment, it is important to monitor the current status of ecosystems and their structural and functional changes in time and space. An essential tool allowing monitoring of ecosystems is remote sensing (RS). Many ecosystems variables are being translated into a spectral response recorded by RS instruments. It is however important to understand the complexity and synergies of the key ecosystem variables influencing the reflected signal. This can be achieved by analysing high resolution RS data from multiple sources acquired simultaneously from the same platform. Such a system has been recently built at CzechGlobe - Global Change Research Institute (The Czech Academy of Sciences). CzechGlobe has been significantly extending its research infrastructure in the last years, which allows advanced monitoring of ecosystem changes at hierarchical levels spanning from molecules to entire ecosystems. One of the CzechGlobe components is a laboratory of imaging spectroscopy. The laboratory is now operating a new platform for advanced remote sensing observations called FLIS (Flying Laboratory of Imaging Spectroscopy). FLIS consists of an airborne carrier equipped with passive RS systems. The core instrument of FLIS is a hyperspectral imaging system provided by Itres Ltd. The hyperspectral system consists of three spectroradiometers (CASI 1500, SASI 600 and TASI 600) that cover the reflective spectral range from 380 to 2450 nm, as well as the thermal range from 8 to 11.5 μm. The airborne platform is prepared for mounting of full-waveform laser scanner Riegl-Q780 as well, however a laser scanner is not a permanent part of FLIS. In 2014 the installation of the hyperspectral scanners was completed and the first flights were carried out with all sensors. The new hyperspectral imaging system required adaptations in the data pre-processing chain. The established pre-processing chain (radiometric, atmospheric and geometric corrections), which was tailored mainly to the AISA Eagle instrument operated at CzechGlobe since 2004, has been now modified to fit the new system and users needs. Continuous development of the processing chain is now focused mainly on establishing pre-processing of thermal data including emissivity estimation and also on joint processing of hyperspectral and laser scanning data.

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15

Udelhoven, Thomas, Martin Schlerf, Karl Segl, Kaniska Mallick, Christian Bossung, Rebecca Retzlaff, Gilles Rock, et al. "A Satellite-Based Imaging Instrumentation Concept for Hyperspectral Thermal Remote Sensing." Sensors 17, no. 7 (July 1, 2017): 1542. http://dx.doi.org/10.3390/s17071542.

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16

Dumont, Jennifer, Tapani Hirvonen, Ville Heikkinen, Maxime Mistretta, Lars Granlund, Katri Himanen, Laure Fauch, et al. "Thermal and hyperspectral imaging for Norway spruce (Picea abies) seeds screening." Computers and Electronics in Agriculture 116 (August 2015): 118–24. http://dx.doi.org/10.1016/j.compag.2015.06.010.

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17

Edelman, G. J., and M. C. Aalders. "Photogrammetry using visible, infrared, hyperspectral and thermal imaging of crime scenes." Forensic Science International 292 (November 2018): 181–89. http://dx.doi.org/10.1016/j.forsciint.2018.09.025.

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18

Chen, Chi-Wen, Yu-Sheng Tseng, Arvind Mukundan, and Hsiang-Chen Wang. "Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging." Applied Sciences 11, no. 10 (May 17, 2021): 4543. http://dx.doi.org/10.3390/app11104543.

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This paper proposes a method to detect air pollution by applying a hyperspectral imaging algorithm for visible light, near infrared, and far infrared. By assigning hyperspectral information to images from monocular, near infrared, and thermal imaging, principal component analysis is performed on hyperspectral images taken at different times to obtain the solar radiation intensity. The Beer–Lambert law and multivariate regression analysis are used to calculate the PM2.5 and PM10 concentrations during the period, which are compared with the corresponding PM2.5 and PM10 concentrations from the Taiwan Environmental Protection Agency to evaluate the accuracy of this method. This study reveals that the accuracy in the visible light band is higher than the near-infrared and far-infrared bands, and it is also the most convenient band for data acquisition. Therefore, in the future, mobile phone cameras will be able to analyze the PM2.5 and PM10 concentrations at any given time using this algorithm by capturing images to increase the convenience and immediacy of detection.

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19

McCarthy, Melissa, Victoria Irene Prete, SeungJu Oh, Garrick Gu, Jorge Lujan-Hernandez, Danielle Stamer, and Janice Lalikos. "The Use of Visible-Light Hyperspectral Imaging in Evaluating Burn Wounds: A Case Report." Journal of Burn Care & Research 42, no. 4 (April 29, 2021): 825–28. http://dx.doi.org/10.1093/jbcr/irab003.

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Abstract Burn depth is a critical factor in determining the healing potential of a burn as the extent of injury ultimately guides overall treatment. Visible-Light Hyperspectral Imaging is an FDA-approved, noninvasive, and noncontrast imaging technology that uses light waves within the visible spectrum to evaluate skin and superficial soft tissue perfusion. In this case report, visible-light hyperspectral imaging was used to evaluate a 37-year-old male who presented to the Emergency Department with a thermal burn of the trunk, back, and right upper extremity. Images were taken at initial evaluation, 6 hours postinjury, and again during daily dressing changes until hospital day 5 when the patient underwent surgical debridement. In this patient, operative treatment was postponed until 89.7 hours postinjury, at which point the clinical examination showed clear visual demarcation in regions of irreversible damage. Comparatively, visible-light hyperspectral imaging analysis of the permanently injured tissue demonstrated acute but varying changes in both oxygenated hemoglobin and deoxygenated hemoglobin at the time of initial evaluation. The most dramatic change in tissue oxygenation occurred between 6.5 and 39.3 hours, demonstrating visible-light hyperspectral imaging’s ability to detect significant differences in oxygenation values between areas of second-degree superficial burns and areas of second-degree deep and third-degree burns in the acute period. The data suggest that the utilization of visible-light hyperspectral imaging in this 6.5- to 39.3-hour window may help predict final burn depth before clinical assessment, potentially allowing for surgical intervention within the first 48 hours following injury.

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20

Idoughi, Ramzi, Thomas H. G. Vidal, Pierre-Yves Foucher, Marc-André Gagnon, and Xavier Briottet. "Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging." Journal of Spectroscopy 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/5428762.

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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.

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21

De Landro, Martina, Ignacio Espíritu García-Molina, Manuel Barberio, Eric Felli, Vincent Agnus, Margherita Pizzicannella, Michele Diana, Emanuele Zappa, and Paola Saccomandi. "Hyperspectral Imagery for Assessing Laser-Induced Thermal State Change in Liver." Sensors 21, no. 2 (January 18, 2021): 643. http://dx.doi.org/10.3390/s21020643.

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This work presents the potential of hyperspectral imaging (HSI) to monitor the thermal outcome of laser ablation therapy used for minimally invasive tumor removal. Our main goal is the establishment of indicators of the thermal damage of living tissues, which can be used to assess the effect of the procedure. These indicators rely on the spectral variation of temperature-dependent tissue chromophores, i.e., oxyhemoglobin, deoxyhemoglobin, methemoglobin, and water. Laser treatment was performed at specific temperature thresholds (from 60 to 110 °C) on in-vivo animal liver and was assessed with a hyperspectral camera (500–995 nm) during and after the treatment. The indicators were extracted from the hyperspectral images after the following processing steps: the breathing motion compensation and the spectral and spatial filtering, the selection of spectral bands corresponding to specific tissue chromophores, and the analysis of the areas under the curves for each spectral band. Results show that properly combining spectral information related to deoxyhemoglobin, methemoglobin, lipids, and water allows for the segmenting of different zones of the laser-induced thermal damage. This preliminary investigation provides indicators for describing the thermal state of the liver, which can be employed in the future as clinical endpoints of the procedure outcome.

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Baranowski, Piotr, Wojciech Mazurek, Joanna Wozniak, and Urszula Majewska. "Detection of early bruises in apples using hyperspectral data and thermal imaging." Journal of Food Engineering 110, no. 3 (June 2012): 345–55. http://dx.doi.org/10.1016/j.jfoodeng.2011.12.038.

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23

De Landro, Martina, Eric Felli, Toby Collins, Richard Nkusi, Andrea Baiocchini, Manuel Barberio, Annalisa Orrico, et al. "Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning." Sensors 21, no. 20 (October 19, 2021): 6934. http://dx.doi.org/10.3390/s21206934.

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Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm “peak temperature prediction model” (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment.

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Shao, Honglan, Chengyu Liu, Chunlai Li, Jianyu Wang, and Feng Xie. "Temperature and Emissivity Inversion Accuracy of Spectral Parameter Changes and Noise of Hyperspectral Thermal Infrared Imaging Spectrometers." Sensors 20, no. 7 (April 8, 2020): 2109. http://dx.doi.org/10.3390/s20072109.

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The emergence of hyperspectral thermal infrared imaging spectrometers makes it possible to retrieve both the land surface temperature (LST) and the land surface emissivity (LSE) simultaneously. However, few articles focus on the problem of how the instrument’s spectral parameters and instrument noise level affect the LST and LSE inversion errors. In terms of instrument development, this article simulated three groups of hyperspectral thermal infrared data with three common spectral parameters and each group of data includes tens of millions of simulated radiances of 1525 emissivity curves with 17 center wavelength shift ratios, 6 full width at half maximum (FWHM) change ratios and 6 noise equivalent differential temperatures (NEDTs) under 15 atmospheric conditions with 6 object temperatures, inverted them by two temperature and emissivity separation methods (ISSTES and ARTEMISS), and analyzed quantitatively the effects of the spectral parameters change and noise of an instrument on the LST and LSE inversion errors. The results show that: (1) center wavelength shifts and noise affect the inversion errors strongly, while FWHM changes affect them weakly; (2) the LST and LSE inversion errors increase with the center wavelength shift ratio in a quadratic function and increase with FWHM change ratio slowly and linearly for both the inversion methods, however they increase with NEDT in an S-curve for ISSTES while they increase with NEDT slightly and linearly for ARTEMISS. During the design and development of a hyperspectral thermal infrared instrument, it is highly recommended to keep the potential center wavelength shift within 1 band and keep NEDT within 0.1K (corresponding LST error < 1K and LSE error < 0.015) for normal applications and within 0.03K (corresponding LST error < 0.5K and LSE error < 0.01) for better application effect and level.

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Zubler, Alanna V., and Jeong-Yeol Yoon. "Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning." Biosensors 10, no. 12 (November 29, 2020): 193. http://dx.doi.org/10.3390/bios10120193.

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Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

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Bourennane, Salah, Caroline Fossati, and Tao Lin. "Noise Removal Based on Tensor Modelling for Hyperspectral Image Classification." Remote Sensing 10, no. 9 (August 21, 2018): 1330. http://dx.doi.org/10.3390/rs10091330.

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With the current state-of-the-art computer aided manufacturing tools, the spatial resolution of hyperspectral sensors is becoming increasingly higher thus making it easy to obtain much more detailed information of the scene captured. However, the improvement of the spatial resolution also brings new challenging problems to address with signal dependent photon noise being one of them. Unlike the signal independent thermal noise, the variance of photon noise is dependent on the signal, therefore many denoising methods developed for the stationary noise cannot be applied directly to the photon noise. To make things worse, both photon and thermal noise coexist in the captured hyperspectral image (HSI), thus making it more difficult to whiten noise. In this paper, we propose a new denoising framework to cope with signal dependent nonwhite noise (SDNW), Pre-estimate—Whitening—Post-estimate (PWP) loop, to reduce both photon and thermal noise in HSI. Previously, we proposed a method based on multidimensional wavelet packet transform and multi-way Wiener filter which performs both white noise and spectral dimensionality reduction, referred to as MWPT-MWF, which was restricted to white noise. We get inspired from this MWPT-MWF to develop a new iterative method for reducing photon and thermal noise. Firstly, the hyperspectral noise parameters estimation (HYNPE) algorithm is used to estimate the noise parameters, the SD noise is converted to an additive white Gaussian noise by pre-whitening procedure and then the whitened HSI is denoised by the proposed method SDNW-MWPT-MWF. As comparative experiments, the Multiple Linear Regression (MLR) based denoising method and tensor-based Multiway Wiener Filter (MWF) are also used in the denoising framework. An HSI captured by Reflective Optics System Imaging Spectrometer (ROSIS) is used in the experiments and the denoising performances are assessed from various aspects: the noise whitening performance, the Signal-to-Noise Ratio (SNR), and the classification performance. The results on the real-world airborne hyperspectral image HYDICE (Hyperspectral Digital Imagery Collection Experiment) are also presented and analyzed. These experiments show that it is worth taking into account noise signal-dependency hypothesis for processing HYDICE and ROSIS HSIs.

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27

Yao, Wu, Xu Mingming, Chen Sujuan, Jiang Yu, Xue Hui, Si Fuqi, and Chen Jiexiang. "Application of thermal/structural/optical integrated analysis to ultraviolet lens of hyperspectral imaging spectrometer." Journal of Applied Optics 37, no. 2 (2016): 262–66. http://dx.doi.org/10.5768/jao201637.0205002.

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28

Idoughi, Ramzi, Thomas H. G. Vidal, Pierre-Yves Foucher, Marc-André Gagnon, and Xavier Briottet. "Corrigendum to “Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging”." Journal of Spectroscopy 2016 (2016): 1–4. http://dx.doi.org/10.1155/2016/4616050.

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29

Pisani, M., P. Bianco, and M. Zucco. "Hyperspectral imaging for thermal analysis and remote gas sensing in the short wave infrared." Applied Physics B 108, no. 1 (April 28, 2012): 231–36. http://dx.doi.org/10.1007/s00340-012-5015-8.

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30

Maimaitiyiming, Matthew, Vasit Sagan, Paheding Sidike, Maitiniyazi Maimaitijiang, Allison J. Miller, and Misha Kwasniewski. "Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology." Remote Sensing 12, no. 19 (October 2, 2020): 3216. http://dx.doi.org/10.3390/rs12193216.

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Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs.

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31

Adegbenjo, Adeyemi O., Li Liu, and Michael O. Ngadi. "Non-Destructive Assessment of Chicken Egg Fertility." Sensors 20, no. 19 (September 28, 2020): 5546. http://dx.doi.org/10.3390/s20195546.

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Total hatching egg set (for both egg production chicks and broilers) in the Agriculture and Agri-Food Canada report 2017 was over 1.0 billion. With the fertility rate for this year observed to be around 82%, there were about 180 million unhatched eggs (worth over 300 million Canadian dollars) incubated in Canada for the year 2017 alone. These non-hatching (non-fertile) eggs can find useful applications as commercial table eggs or low-grade food stock if they can be detected early and isolated accordingly preferably prior to incubation. The conventional method of chicken egg fertility assessment termed candling, is subjective, cumbersome, slow, and eventually inefficient, leading to huge economic losses. Hence, there is a need for a non-destructive, fast and online prediction technology to assist with early chicken egg fertility identification problem. This paper reviewed existing non-destructive approaches including ultrasound and dielectric measurements, thermal imaging, machine vision, spectroscopy, and hyperspectral imaging. Hyperspectral imaging was extensively discussed, being an emerging new technology with great potential. Suggestions were finally proffered towards building futuristic robust model(s) for early detection of chicken egg fertility.

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32

Rahman, Anisur, and Byoung-Kwan Cho. "Assessment of seed quality using non-destructive measurement techniques: a review." Seed Science Research 26, no. 4 (December 2016): 285–305. http://dx.doi.org/10.1017/s0960258516000234.

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AbstractSeed quality is of great importance in optimizing the cost of crop establishment. Rapid and non-destructive seed quality detection methods must therefore be developed for agriculture and the seed production industry. This review focuses primarily on non-destructive techniques, namely machine vision, spectroscopy, hyperspectral imaging, soft X-ray imaging, thermal imaging and electronic nose techniques, for assessing the quality of agricultural seeds. The fundamentals of these techniques are introduced. Seed quality, including chemical composition, variety identification and classification, insect damage and disease assessment as well as seed viability and germinability of various seeds are discussed. We conclude that non-destructive techniques are accurate detection methods with great potential for seed quality assessment.

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33

Xiao, Chunlei, Bihong Fu, Hanqing Shui, Zhaocheng Guo, and Jurui Zhu. "Detecting the Sources of Methane Emission from Oil Shale Mining and Processing Using Airborne Hyperspectral Data." Remote Sensing 12, no. 3 (February 6, 2020): 537. http://dx.doi.org/10.3390/rs12030537.

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Methane (CH4) is one of important greenhouse gases that affects the global radiative balance after carbon dioxide (CO2). Previous studies have demonstrated the detection of known sources of CH4 emission using the hyperspectral technology based on in situ vertical CH4 profile or ground CH4 emissions data. However, those approaches have not yet to detect the unknown terrestrial sources of CH4 emission at local-scale or regional-scale. In this paper, the Shortwave Airborne Spectrographic Imager (SASI) was employed to detect concentrated sources of CH4 emissions based on the absorption of CH4 in the shortwave infrared (SWIR) region. As a result, a band ratio (namely RCH4, RCH4 = Band91/Band78) determined through wavelet transform singularity detection has proposed for detection of the terrestrial CH4 emissions sources using SASI hyperspectral radiance image data, and elevated CH4 locations in the oil shale retorting plants were identified. Additionally, SASI surface reflectance data and multiple reference spectra in the spectral angle mapper (SAM) were used to classify surface sources of CH4 release. High-resolution Google Earth imagery and thermal imaging camera (FLIR GF320) had also verified that the CH4 releasing sources are mainly the oil shale mining field and the retorting plant. Therefore, the high-resolution imaging hyperspectral spectrometer can provide a powerful tool for detecting terrestrial CH4 release sources at local-scale to reduce the greenhouse gas emissions related to hydrocarbon development.

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Ma, Kirsten F., Thomas S. Nijboer, Simone F. Kleiss, Mostafa El Moumni, Reinoud P. H. Bokkers, Richte C. L. Schuurmann, and Jean-Paul P. M. de de Vries. "Determination of Changes in Tissue Perfusion at Home with Hyperspectral and Thermal Imaging in the First Six Weeks after Endovascular Therapy in Patients with Peripheral Arterial Disease." Diagnostics 12, no. 10 (October 14, 2022): 2489. http://dx.doi.org/10.3390/diagnostics12102489.

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The aims of this study were to assess changes in tissue perfusion up to 6 weeks after endovascular therapy (EVT), in hospital and at home, and to determine differences in tissue perfusion between patients with and without clinical improvement or good angiographic result. This single-center prospective cohort study included patients undergoing EVT for Rutherford stages two to six. Hyperspectral and thermal imaging were performed at the dorsal and plantar sides of the foot. These measurements consisted of a baseline measurement pre-EVT, and six follow-up measurements obtained at 1 and 4 h and 6 weeks in hospital, and 1 day, 7 days, and 14 days at home. Clinical improvement was defined as a decrease of one or more Rutherford class or decrease in the wound surface area and a good angiographic result was accomplished when a Transatlantic Inter-Society Consensus for the Management of PAD II C or D lesion was treated and uninterrupted flow continued in at least one below-the-knee artery in continuation with the inframalleolar arteries. The study included 34 patients with 41 treated limbs. Deoxyhemoglobin values were lower 1 h post-EVT compared with baseline and increased over time up to 6 weeks post-EVT. Significant differences in deoxyhemoglobin levels at 7 and 14 days post-EVT were determined between patients with and without clinical or angiographic success. This prospective pilot study shows the feasibility of hyperspectral imaging and thermal imaging post-EVT at home, which may decrease the need for hospital visits.

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35

Nischwitz, Sebastian P., Hanna Luze, Marlies Schellnegger, Simon J. Gatterer, Alexandru-Cristian Tuca, Raimund Winter, and Lars-Peter Kamolz. "Thermal, Hyperspectral, and Laser Doppler Imaging: Non-Invasive Tools for Detection of The Deep Inferior Epigastric Artery Perforators—A Prospective Comparison Study." Journal of Personalized Medicine 11, no. 10 (October 5, 2021): 1005. http://dx.doi.org/10.3390/jpm11101005.

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Perforator flaps have become one of the leading procedures in microsurgical tissue transfer. Individual defects require a tailored approach to guarantee the most effective treatment. A thorough understanding of the individual vascular anatomy and the location of prominent perforators is of utmost importance and usually requires invasive angiography or at least acoustic Doppler exploration. In this study, we aimed at evaluating different non-invasive imaging modalities as possible alternatives for perforator location detection. After a cooling phase, we performed thermal, hyperspectral and Laser Doppler imaging and visually evaluated a possible detection of the perforator for a period of five minutes with an image taken every minute. We identified the most prominent perforator of the deep inferior epigastric artery by handheld acoustic Doppler in 18 patients. The detected perforator locations were then correlated. Eighteen participants were assessed with six images each per imaging method. We could show a positive match for 94.44%, 38.89%, and 0% of patients and 92.59%, 25.93%, and 0% of images for the methods respectively compared to the handheld acoustic Doppler. Sex, age, abdominal girth, and BMI showed no correlation with a possible visual detection of the perforator in the images. Therefore, thermal imaging can yield valuable supporting data in the individualized procedure planning. Future larger cohort studies are required to better assess the full potential of modern handheld thermal imaging devices.

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36

李, 朋朋. "Nondestructive Detecting Method for Moisture Content of Abalone during Thermal Processing Based on Hyperspectral Imaging." Journal of Sensor Technology and Application 10, no. 02 (2022): 236–45. http://dx.doi.org/10.12677/jsta.2022.102029.

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37

Ruane, R. A., K. A. J. Doherty, R. Dorrepaal, B. Twomey, A. Gowen, J. Flanagan, D. de Faoite, and K. T. Stanton. "Hyperspectral imaging with unsupervised pattern recognition: A novel surface characterization technique for thermal control coatings." Materials Letters 254 (November 2019): 273–77. http://dx.doi.org/10.1016/j.matlet.2019.07.085.

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38

Lu, Yuzhen, and Renfu Lu. "Non-Destructive Defect Detection of Apples by Spectroscopic and Imaging Technologies: A Review." Transactions of the ASABE 60, no. 5 (2017): 1765–90. http://dx.doi.org/10.13031/trans.12431.

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Abstract. Apples are susceptible to a wide range of defects that can occur in the orchard and during the post-harvest period. Detection of these defects by non-destructive sensing techniques is of great importance for the apple industry and has been an intensive research topic over the past two decades. This review presents an overview of common defects in apples, encompassing physiological disorders, mechanical damage, pathological disorders, and contamination. Presented and discussed in this review is research progress on the detection of defects in apples using various non-destructive spectroscopic and imaging techniques, including visible/near-infrared spectroscopy, fluorescence spectroscopy and imaging, monochromatic and color imaging, hyperspectral and multispectral imaging, x-ray imaging, magnetic resonance imaging, thermal imaging, time-resolved and spatially resolved spectroscopy, Raman spectroscopy, biospeckle imaging, and structured-illumination reflectance imaging. This review concludes with remarks on the prospects of these techniques and research needs in the future. Keywords: Apples, Defects, Imaging, Non-destructive detection, Quality, Safety, Spectroscopy.

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Kamińska, Magdalena, Beata Krusiec-Świdergoł, Weronika Pawełczyk, Magdalena Hartman-Petrycka, Anna Banyś, Krzysztof Jonderko, Agata Lebiedowska, Robert Koprowski, and Sławomir Wilczyński. "Application of the Hyperspectral Imaging Method to Assess the Effectiveness of Permanent Makeup Removal." Applied Sciences 13, no. 4 (February 11, 2023): 2330. http://dx.doi.org/10.3390/app13042330.

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The growing popularity of permanent makeup is associated with an increase in the need for treatments that effectively remove pigments from the skin. The risk of the radiation wavelength not being adjusted to the spectral ranges of individual pigments that compose the final color appears because complex dye compositions are used for permanent makeup. Incorrectly selected laser parameters may reduce the effectiveness of the treatment and increase the risk of thermal damage, leading to scarring and discoloration. The aim of the study was to evaluate the effectiveness of laser permanent makeup removal using hyperspectral imaging. Five different dyes were implemented into the skin of the pig’s ear. Then the dyes were removed using a Q-switched Nd:YAG laser. During the study, skin measurements were performed with a hyperspectral camera before and after laser application. The results were analyzed using GLCM, reflectance, and quadratic tree decomposition. Based on the obtained results, it was found that the most difficult dye to remove is white because it is characterized by high reflectivity of electromagnetic radiation. After the dye removal, there was a decrease in reflectance and contrast of GLCM and an increase in homogeneity for all dyes except for white. After the laser application, the pigments were distributed more equally in the skin.

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40

Hassoun, Abdo, Karsten Heia, Stein-Kato Lindberg, and Heidi Nilsen. "Spectroscopic Techniques for Monitoring Thermal Treatments in Fish and Other Seafood: A Review of Recent Developments and Applications." Foods 9, no. 6 (June 10, 2020): 767. http://dx.doi.org/10.3390/foods9060767.

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Cooking is an important processing method, that has been used since ancient times in order to both ensure microbiological safety and give desired organoleptic properties to the cooked food. Fish and other seafood products are highly sensitive to thermal treatments and the application of severe heat can result in negative consequences on sensory and nutritional parameters, as well as other quality attributes of the thermally processed products. To avoid such undesired effects and to extend the shelf life of these perishable products, both the heat processing methods and the assessment techniques used to monitor the process should be optimized. In this review paper, the most common cooking methods and some innovative ones will first be presented with a brief discussion of their impact on seafood quality. The main methods used for monitoring heat treatments will then be reviewed with a special focus on spectroscopic techniques, which are known to be rapid and non-destructive methods compared to traditional approaches. Finally, viewpoints of the current challenges will be discussed and possible directions for future applications and research will be suggested. The literature presented in this review clearly demonstrates the potential of spectroscopic techniques, coupled with chemometric tools, for online monitoring of heat-induced changes resulting from the application of thermal treatments of seafood. The use of fluorescence hyperspectral imaging is especially promising, as the technique combines the merits of both fluorescence spectroscopy (high sensitivity and selectivity) and hyperspectral imaging (spatial dimension). With further research and investigation, the few current limitations of monitoring thermal treatments by spectroscopy can be addressed, thus enabling the use of spectroscopic techniques as a routine tool in the seafood industry.

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41

Weber, I., A. Jenal, C. Kneer, and J. Bongartz. "GYROCOPTER-BASED REMOTE SENSING PLATFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1333–37. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1333-2015.

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In this paper the development of a lightweight and highly modularized airborne sensor platform for remote sensing applications utilizing a gyrocopter as a carrier platform is described. The current sensor configuration consists of a high resolution DSLR camera for VIS-RGB recordings. As a second sensor modality, a snapshot hyperspectral camera was integrated in the aircraft. Moreover a custom-developed thermal imaging system composed of a VIS-PAN camera and a LWIR-camera is used for aerial recordings in the thermal infrared range. Furthermore another custom-developed highly flexible imaging system for high resolution multispectral image acquisition with up to six spectral bands in the VIS-NIR range is presented. The performance of the overall system was tested during several flights with all sensor modalities and the precalculated demands with respect to spatial resolution and reliability were validated. The collected data sets were georeferenced, georectified, orthorectified and then stitched to mosaics.

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42

Schläpfer, D., R. Richter, C. Popp, and P. Nygren. "DROACOR®-THERMAL: AUTOMATED TEMPERATURE / EMISSIVITY RETRIEVAL FOR DRONE BASED HYPERSPECTRAL IMAGING DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 429–34. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-429-2022.

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Abstract. Thermal remote sensing from unmanned aerial vehicles is a slowly but steadily growing field of application. New hyperspectral systems operating in the thermal infrared are deployable on such systems and are also usable for ground based monitoring, such as in mining applications. Temperature/emissivity retrieval methods have to be adapted for these new situations. This contribution presents an extension of the Drone Atmospheric Correction method (DROACOR®) for thermal infrared imaging spectroscopy. The method includes an implementation of the semi-automatic normalized emissivity mapping (NEM) method for temperature/ emissivity separation. Furthermore, an extension of the method for correction of low emissivity targets, appearing as cold targets in the temperature mapping is introduced. Two examples of DROACOR-thermal processing are presented for a nadir looking drone based and a horizontal ground based data acquisitions are shown. The resulting spectral emissivitiy distributions and temperature mappings are plausible. They are well comparable to spectral library references and allow for the detection of materials only visible in the thermal infrared range.

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43

Chavez-Angel, Emigdio, Ryan C. Ng, Susanne Sandell, Jianying He, Alejandro Castro-Alvarez, Clivia M. Sotomayor Torres, and Martin Kreuzer. "Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films." Polymers 15, no. 3 (January 19, 2023): 536. http://dx.doi.org/10.3390/polym15030536.

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The thermal imaging of surfaces with microscale spatial resolution over micro-sized areas remains a challenging and time-consuming task. Surface thermal imaging is a very important characterization tool in mechanical engineering, microelectronics, chemical process engineering, optics, microfluidics, and biochemistry processing, among others. Within the realm of electronic circuits, this technique has significant potential for investigating hot spots, power densities, and monitoring heat distributions in complementary metal–oxide–semiconductor (CMOS) platforms. We present a new technique for remote non-invasive, contactless thermal field mapping using synchrotron radiation-based Fourier-transform infrared microspectroscopy. We demonstrate a spatial resolution better than 10 um over areas on the order of 12 000 um2 measured in a polymeric thin film on top of CaF2 substrates. Thermal images were obtained from infrared spectra of poly(methyl methacrylate) thin films heated with a wire. The temperature dependence of the collected infrared spectra was analyzed via linear regression and machine learning algorithms, namely random forest and k-nearest neighbor algorithms. This approach speeds up signal analysis and allows for the generation of hyperspectral temperature maps. The results here highlight the potential of infrared absorbance to serve as a remote method for the quantitative determination of heat distribution, thermal properties, and the existence of hot spots, with implications in CMOS technologies and other electronic devices.

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44

Levy, Joseph, Anne Nolin, Andrew Fountain, and James Head. "Hyperspectral measurements of wet, dry and saline soils from the McMurdo Dry Valleys: soil moisture properties from remote sensing." Antarctic Science 26, no. 5 (February 14, 2014): 565–72. http://dx.doi.org/10.1017/s0954102013000977.

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AbstractSoil moisture is a spatially heterogeneous quantity in the McMurdo Dry Valleys of Antarctica that exerts a large influence on the biological community and on the thermal state of Dry Valleys permafrost. The goal of this project was to determine whether hyperspectral remote sensing techniques could be used to determine soil moisture conditions in the Dry Valleys. We measured the spectral reflectance factors of wetted soil samples from the Dry Valleys under natural light conditions and related diagnostic spectral features to surface layer soil moisture content. Diagnostic water absorption features in the spectra at 1.4 µm and 1.9 µm were present in all samples, including samples doped with high concentrations of chloride salts. The depth of the 1.4 µm absorption is shown to increase linearly with increasing gravimetric water content. These results suggest that airborne hyperspectral imaging of the Dry Valleys could generate soil moisture maps of this environment over large spatial areas using non-invasive remote-sensing techniques.

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45

Mansfield, Colin D., E. Michael Attas, and Richard M. Gall. "Evaluation of Static Thermal and Near-Infrared Hyperspectral Imaging for the Diagnosis of Acute Maxillary Rhinosinusitis." Journal of Otolaryngology 34, no. 02 (2005): 99. http://dx.doi.org/10.2310/7070.2005.04056.

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46

Myronycheva, Olena, Ekaterina Sidorova, Olle Hagman, Margot Sehlstedt-Persson, Olov Karlsson, and Dick Sandberg. "Hyperspectral Imaging Surface Analysis for Dried and Thermally Modified Wood: An Exploratory Study." Journal of Spectroscopy 2018 (November 14, 2018): 1–10. http://dx.doi.org/10.1155/2018/7423501.

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Naturally seasoned, kiln-dried, and thermally modified wood has been studied by hyperspectral near-infrared imaging between 980 and 2500 nm in order to obtain spatial chemical information. Evince software was used to explore, preprocess, and analyse spectral data from image pixels and link these data to chemical information via spectral wavelength assignment. A PCA model showed that regions with high absorbance were related to extractives with phenolic groups and aliphatic hydrocarbons. The sharp wavelength band at 2135 nm was found by multivariate analysis to be useful for multivariate calibration. This peak represents the largest variation that characterizes the knot area and can be related to areas in wood rich in hydrocarbons and phenol, and it can perhaps be used for future calibration of other wood surfaces. The discriminant analysis of thermally treated wood showed the strongest differentiation between the planed and rip-cut wood surfaces and a fairly clear discrimination between the two thermal processes. The wavelength band at 2100 nm showed the greatest difference and may correspond to stretching of C=O-O of polymeric acetyl groups, but this requires confirmation by chemical analysis.

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47

Hassoun, Abdo, Janna Cropotova, Turid Rustad, Karsten Heia, Stein-Kato Lindberg, and Heidi Nilsen. "Use of Spectroscopic Techniques for a Rapid and Non-Destructive Monitoring of Thermal Treatments and Storage Time of Sous-Vide Cooked Cod Fillets." Sensors 20, no. 8 (April 23, 2020): 2410. http://dx.doi.org/10.3390/s20082410.

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In this work, the potential of spectroscopic techniques was studied to investigate heat-induced changes occurring during the application of thermal treatments on cod (Gadus morhua L.) fillets. Vacuum-packed samples were thermally treated in a water bath at 50, 60, 70 and 80 °C for 5 and 10 min, and further stored for one, four, and eight days at 4 ± 1 °C before analysis. Several traditional (including cooking loss, drip loss, texture, protein solubility, protein oxidation, and color) and spectroscopic (fluorescence and diffuse reflectance hyperspectral imaging) measurements were conducted on the same samples. The results showed a decrease in fluorescence intensity with increasing cooking temperature and storage time, while the impact of cooking time was only noticeable at low temperatures. Diffuse reflectance data exhibited a decrease in absorbance, possibly as a result of protein denaturation and increased scattering at higher cooking temperatures. Both fluorescence and diffuse reflectance data were highly correlated with color parameters, whereas moderate correlations were observed with most other traditional parameters. Support vector machine models performed better than partial least square ones for both classification of cod samples cooked at different temperatures and in prediction of the cooking temperature. The best classification result was obtained on fluorescence data, achieving an accuracy of 92.5%, while the prediction models resulted in a root mean square error of prediction of cooking temperature lower than 5 °C. Overall, the classification and prediction models showed good results, indicating that spectroscopic techniques, especially fluorescence hyperspectral imaging, have a high potential for monitoring thermal treatments in cod fillets.

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Honda, Reo, Meguya Ryu, Masayuki Moritake, Armandas Balčytis, Vygantas Mizeikis, Jitraporn Vongsvivut, Mark J. Tobin, et al. "Infrared Polariscopy Imaging of Linear Polymeric Patterns with a Focal Plane Array." Nanomaterials 9, no. 5 (May 13, 2019): 732. http://dx.doi.org/10.3390/nano9050732.

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Polariscopy is demonstrated using hyperspectral imaging with a focal plane array (FPA) detector in the infrared (IR) spectral region under illumination by thermal and synchrotron light sources. FPA Fourier-transform IR (FTIR) imaging microspectroscopy is useful for monitoring real time changes at specific absorption bands when combined with a high brightness synchrotron source. In this study, several types of samples with unique structural motifs were selected and used for assessing the capability of polariscopy under this FPA-FTIR imaging technique. It was shown that the time required for polariscopy at IR wavelengths can be substantially reduced by the FPA-FTIR imaging approach. By using natural and laser fabricated polymers with sub-wavelength features, alignment of absorbing molecular dipoles and higher order patterns (laser fabricated structures) were revealed. Spectral polariscopy at the absorption peaks can reveal the orientation of sub-wavelength patterns (even when they are not spatially resolved) or the orientation of the absorbing dipoles.

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Gerhards, Max, Martin Schlerf, Kaniska Mallick, and Thomas Udelhoven. "Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review." Remote Sensing 11, no. 10 (May 24, 2019): 1240. http://dx.doi.org/10.3390/rs11101240.

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Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.

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Baranowski, Piotr, Malgorzata Jedryczka, Wojciech Mazurek, Danuta Babula-Skowronska, Anna Siedliska, and Joanna Kaczmarek. "Hyperspectral and Thermal Imaging of Oilseed Rape (Brassica napus) Response to Fungal Species of the Genus Alternaria." PLOS ONE 10, no. 3 (March 31, 2015): e0122913. http://dx.doi.org/10.1371/journal.pone.0122913.

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