Добірка наукової літератури з теми "IR radiometric processing"

Thermography is probably the most used method of measuring surface temperature by analyzing radiation in the infrared part of the spectrum which accuracy depends on factors such as emissivity and reflected radiation. Contrary to popular belief that thermographic images represent temperature maps, they are actually thermal radiation converted into an image, and if not properly calibrated, they show incorrect temperatures. The objective of this study is to analyze commonly used image processing techniques and their impact on radiometric data in thermography. In particular, the extent to which a thermograph can be considered as an image and how image processing affects radiometric data. Three analyzes are presented in the paper. The first one examines how image processing techniques, such as contrast and brightness, affect physical reality and its representation in thermographic imaging. The second analysis examines the effects of JPEG compression on radiometric data and how degradation of the data varies with the compression parameters. The third analysis aims to determine the optimal resolution increase required to minimize the effects of compression on the radiometric data. The output from an IR camera in CSV format was used for these analyses, and compared to images from the manufacturer’s software. The IR camera providing data in JPEG format was used, and the data included thermographic images, visible images, and a matrix of thermal radiation data. The study was verified with a reference blackbody radiation set at 60 °C. The results highlight the dangers of interpreting thermographic images as temperature maps without considering the underlying radiometric data which can be affected by image processing and compression. The paper concludes with the importance of accurate and precise thermographic analysis for reliable temperature measurement.

In the present research an attempt was made to extract information obtained on the basis of IR radiometric measurements in order to determine content of moisture and aerosol in the atmosphere, as well as height of the cloud base. Particular attention was paid to the method of measurement and processing and presentation of the results.

Space target infrared (IR) characteristics model can be used to design space target detection sensor, generating simulation data to validate the data processing algorithms, such as target detection and tracking. In the work, a HEO target IR characteristics model is built. The model consists of the geometry module and the IR radiometric module. Unlike the traditional space IR model, the temperature of cabin inner is used as the simulation origin. Using the model, the irradiance spectra of HEO target are calculated for different target temperature. With the calculation results, the main wavelength range for HEO detection is analyzed. Using the equvalent temperature, the work also designs a simulative target, which has the similar IR characteristics of a HEO target. The simulator can be used in the ground test for the imaging sensor or target decoy.

Current methods of measuring heart rate (HR) and oxygen levels (SPO2) require physical contact, are individualised, and for accurate oxygen levels may also require a blood test. No-touch or non-invasive technologies are not currently commercially available for use in healthcare settings. To date, there has been no assessment of a system that measures HR and SPO2 using commercial off-the-shelf camera technology that utilises R, G, B, and IR data. Moreover, no formal remote photoplethysmography studies have been performed in real-life scenarios with participants at home with different demographic characteristics. This novel study addresses all these objectives by developing, optimising, and evaluating a system that measures the HR and SPO2 of 40 participants. HR and SPO2 are determined by measuring the frequencies from different wavelength band regions using FFT and radiometric measurements after pre-processing face regions of interest (forehead, lips, and cheeks) from colour, IR, and depth data. Detrending, interpolating, hamming, and normalising the signal with FastICA produced the lowest RMSE of 7.8 for HR with the r-correlation value of 0.85 and RMSE 2.3 for SPO2. This novel system could be used in several critical care settings, including in care homes and in hospitals and prompt clinical intervention as required.

Abstract. Infrared (IR) cameras based on microbolometer focal plane arrays (FPAs) are the most widely used cameras in thermography. New fields of applications like handheld devices and small distributed sensors benefit from the latest sensor improvements in terms of cost and size reduction. In order to compensate for disturbing influences derived from changing ambient conditions, radiometric cameras use an optical shutter for online recalibration purposes, partially also together with sensor temperature stabilization. For these new applications, IR cameras should consist only of infrared optics, a sensor array, and digital signal processing (DSP). For acceptable measurement uncertainty values without using an optical shutter (shutter-less), the disturbing influences of changing thermal conditions have to be treated based on temperature measurements of the camera interior. We propose a compensation approach based on calibration measurements under controlled ambient conditions. All correction parameters are determined during the calibration process. Without sensor temperature stabilization (TEC-less), the pixel responsivity is also affected by the camera temperature changes and has to be considered separately. This paper presents the details of the compensation procedure and discusses relevant aspects to gain low temperature measurement uncertainty. The residual measurement uncertainty values are compared to the shutter-based compensation approach.

Medical infrared thermography has proven to be a complementary procedure to physiological disorders, such as the diabetic foot. However, the technique remains essentially based on 2D images that display partial anatomy. In this context, a 3D thermal model provides improved visualization and faster inspection. This paper presents a 3D reconstruction method associated with temperature information. The proposed solution is based on a Structure from Motion and Multi-view Stereo approach, exploiting a set of multimodal merged images. The infrared images were obtained by automatically processing the radiometric data to remove thermal interferences, segment the RoI, enhance false-color contrast, and for multimodal co-registration under a controlled environment and a ∆T < 2.6% between the RoI and thermal interferences. The geometric verification accuracy was 77% ± 2%. Moreover, a normalized error was adjusted per sample based on a linear model to compensate for the curvature emissivity (error ≈ 10% near to 90°). The 3D models were displayed with temperature information and interaction controls to observe any point of view. The temperature sidebar values were assigned with information retrieved only from the RoI. The results have proven the feasibility of the 3D multimodal construction to be used as a promising tool in the diagnosis of diabetic foot.

Infrared sounding measurements of the Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and High-resolution Infrared Radiation Sounder/2 (HIRS/2) instruments are used to recalibrate infrared (IR; ~11 µm) channels and water vapor (WV; ~6 µm) channels of the Visible and Infrared Spin Scan Radiometer (VISSR), Japanese Advanced Meteorological Imager (JAMI), and IMAGER instruments onboard the historical geostationary satellites of the Japan Meteorological Agency (JMA). The recalibration was performed using a common recalibration method developed by European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), which can be applied to the historical geostationary satellites to produce Fundamental Climate Data Records (FCDR). Pseudo geostationary imager radiances were computed from the infrared sounding measurements and regressed against the radiances from the geostationary satellites. Recalibration factors were computed from these pseudo imager radiance pairs. This paper presents and evaluates the result of recalibration of longtime-series of IR (1978–2016) and WV (1995–2016) measurements from JMA’s historical geostationary satellites. For the IR data of the earlier satellites (Geostationary Metrological Satellite (GMS) to GMS-4) significant seasonal variations in radiometric biases were observed. This suggests that the sensors on GMS to GMS-4 were strongly affected by seasonal variations in solar illumination. The amplitudes of these seasonal variations range from 3 K for the earlier satellites to <0.4 K for the recent satellites (GMS-5, Geostationary Operational Environmental Satellite-9 (GOES-9), Multi-functional Transport Satellite-1R (MTSAT-1R) and MTSAT-2). For the WV data of GOES-9, MTSAT-1R and MTSAT-2, no seasonal variations in radiometric biases were observed. However, for GMS-5, the amplitude of seasonal variation in bias was about 0.5 K. Overall, the magnitude of the biases for GMS-5, MTSAT-1R and MTSAT-2 were smaller than 0.3 K. Finally, our analysis confirms the existence of errors due to atmospheric absorption contamination in the operational Spectral Response Function (SRF) of the WV channel of GMS-5. The method used in this study is based on the principles developed within Global Space-based Inter-calibration System (GSICS). Moreover, presented results contribute to the Inter-calibration of imager observations from time-series of geostationary satellites (IOGEO) project under the umbrella of the World Meteorological Organization (WMO) initiative Sustained and Coordinated Processing of Environmental Satellite data for Climate Monitoring (SCOPE-CM).

Abstract. The calibration of SCIAMACHY was thoroughly checked since the instrument was launched on-board ENVISAT in February 2002. While SCIAMACHY's functional performance is excellent since launch, a number of technical difficulties have appeared, that required adjustments to the calibration. The problems can be separated into three types: (1) Those caused by the instrument and/or platform environment. Among these are the high water content in the satellite structure and/or MLI layer. This results in the deposition of ice on the detectors in channels 7 and 8 which seriously affects the retrievals in the IR, mostly because of the continuous change of the slit function caused by scattering of the light through the ice layer. Additionally a light leak in channel 7 severely hampers any retrieval from this channel. (2) Problems due to errors in the on-ground calibration and/or data processing affecting for example the radiometric calibration. A new approach based on a mixture of on-ground and in-flight data is shortly described here. (3) Problems caused by principal limitations of the calibration concept, e.g. the possible appearance of spectral structures after the polarisation correction due to unavoidable errors in the determination of atmospheric polarisation. In this paper we give a complete overview of the calibration and problems that still have to be solved. We will also give an indication of the effect of calibration problems on retrievals where possible. Since the operational processing chain is currently being updated and no newly processed data are available at this point in time, for some calibration issues only a rough estimate of the effect on Level 2 products can be given. However, it is the intention of this paper to serve as a future reference for detailed studies into specific calibration issues.

Abstract. All over the world, road infrastructures are getting closer to their life cycle and need to be constantly inspected: a consistent number of bridges are structurally deficient, and the risk of collapse can no longer be excluded. In contrast with the past, the interest in structure durability has recently grown rapidly. In order to make bridges durable, it is necessary to carry out ordinary maintenance, preceded by inspection activities, which can be traditionally divided in two categories: destructive and non-destructive (NDT). All the NDT inspections (visual, IR thermography, GPR) can be conducted by using UAS (Unmanned Aerial Systems), a technology that makes bridges inspections quicker, cheaper, objective and repeatable. This study presents the visual inspection and survey of two bridges by using a UAS DJI Mavic 2 Pro, equipped with a 20Mpixel Hasselblad camera that records 60fps 4K video and a 10bit radiometric resolution. Starting from the acquired data, a 3D model of each structure was built by using Structure from Motion (SfM) principles and software. To validate the two models, each of them characterized by a centimetric accuracy, the UAS camera generated cloud of points and was co-registered with the point cloud of a terrestrial laser-scanner using Ground Control Points (GCPs). To make this, CloudCompare comparison software was used; the plugin M3C2 automatically calculates the distance between the points of two compared clouds. Finally, some general rules concerning the UAS main characteristics for inspection of bridges and software for data processing are proposed.

Atmospheric correction is a necessary step in processing data recorded by spaceborne sensors for cloudless atmosphere, primarily in the visible and near-IR spectral range. In this paper we present a fast and sufficiently accurate method of atmospheric correction based on the analytical solutions of radiative transfer equation (RTE). The proposed analytical equations can be used to calculate the spectrum of outgoing radiation at the top boundary of the cloudless atmosphere. The solution of the inverse problem for finding unknown parameters of the model is carried out by the method of non-linear least squares (Levenberg-Marquardt algorithm) for an individual selected pixel of the image, taking into account the adjacency effects. Using the found parameters of the atmosphere and the average surface reflectance, and also assuming homogeneity of the atmosphere within a certain area of the hyperspectral image (or within the whole frame), the spectral reflectance at the Earth’s surface is calculated for all other pixels. It is essential that the procedure of the numerical simulation using non-linear least squares is based on the analytical solution of the direct transfer problem. This enables fast solution of the inverse problem in a very short calculation time. Testing of the method has been performed using the synthetic outgoing radiation spectra at the top of atmosphere, obtained from the LibRadTran code. In addition, we have used the spectra measured by the Hyperion. A comparison with the results of atmospheric correction in module FLAASH of ENVI package has been performed. Finally, to validate data obtained by our method, a comparative analysis with ground-based measurements of the Radiometric Calibration Network (RadCalNet) was carried out.

La thermographie infrarouge médicale est une méthode quantitative permettant d'identifier une température irrégulière à des fins de diagnostic médical. Une température corporelle anormale étant un signe naturel de maladie, les données de cette modalité peuvent être utilisées pour détecter des maladies ou des anomalies physiologiques, comme le pied diabétique qui est le sujet de cette thèse Selon la Fédération internationale du diabète, près d'un demi-million de personnes ont été diagnostiquées avec un diabète sucré en 2019. La neuropathie périphérique peut toucher 40 % à 60 % des individus en raison de problèmes liés au pied diabétique. L'amputation sous l'articulation du genou en tant qu'opération préventive est un risque courant chez ces personnes, et on estime qu'une amputation se produit toutes les 30 secondes dans le monde. Actuellement, l'IRM, la radiographie et la thermographie, ainsi que les techniques de traitement de l'image, font partie des modalités d'imagerie médicale utilisées pour diagnostiquer précocement le pied diabétique. La thermographie infrarouge médicale, quant à elle, est une approche passive sans contact, non invasive et non ionisante. L'imagerie infrarouge du pied diabétique repose encore principalement sur des images 2D qui ne montrent qu'une partie de l'anatomie. Dans ce scénario, un modèle thermique 3D permettrait une meilleure observation et inspection de la région d'intérêt, qui comprend les zones plantaires, latérale et dorsale. L'utilisation de la modélisation 3D pour le diagnostic du pied diabétique a été documentée dans quelques articles au moment de la publication de cette thèse.La méthode proposée utilise une série d'images infrarouges et à spectre visible fusionnées comme données d'entrée pour l'estimation du nuage de points 3D et la reconstruction de surface, basée sur les méthodes Structure from Motion et Multi-view Stereo. Cependant, la segmentation dans les images thermiques est une tâche qui reste manuelle puisque la détection des caractéristiques descriptives est presque impossible dans les images fausses couleurs. Par conséquent, cette thèse présente une méthode de segmentation automatique basée sur le traitement des informations radiométriques avant de générer une image en fausses couleurs. Le traitement des données radiométriques est une alternative au traitement d'image numérique en raison de la possibilité d'éliminer les interférences thermiques (par exemple la lampe, les ombres thermiques, ou même les parties du corps du patient) sur la base de critères de seuil de température, d'améliorer le contraste des couleurs, de segmenter la région d'intérêt, et de combiner sur des images du spectre visible.Les images multimodales fusionnées ont été utilisées comme informations d'entrée pour l'estimation de la surface 3D du pied. Le modèle obtenu était doté d'une échelle de température liée aux données radiométriques obtenues par chaque volontaire, ainsi que de la possibilité de faire pivoter le modèle pour observer chaque point de vue. Les résultats montrent que le modèle multimodal 3D est réalisable, permettant une visualisation meilleure et plus rapide de la distribution de la température lors du diagnostic du pied diabétique. La contribution de cette thèse concerne l'acquisition d'un modèle 3D avec des informations thermiques et la segmentation automatique dans les images thermiques pour la fusion multimodale. La perspective est la validation clinique pour tester l'assistance dans le diagnostic du pied diabétique. Cependant, du point de vue expérimental/théorique, il est envisagé d'étudier la précision du recalage des images avec la méthode proposée de segmentation automatique, et la précision thermique et spatiale des modèles 3D réalisés avec des fantômes
Medical infrared thermography is a quantitative method for identifying irregular temperatures for medical diagnosis. Because abnormal body temperature is a natural sign of illness, this modality's data can be used to detect disease or physiological abnormalities, such as diabetic foot which is the subject of this thesis According to the International Diabetes Federation, nearly half a million people were diagnosed with diabetes mellitus in 2019. Peripheral neuropathy may affect 40 % to 60 % of individuals because of diabetic foot issues. Amputation below the knee joint as a preventive operation is a common risk among these individuals, and it is estimated that one amputation occurs every 30 seconds around the world. Currently, MRI, radiography, and thermography, together with image processing techniques, are among the medical imaging modalities utilized to diagnose the diabetic foot early. Medical infrared thermography, on the other hand, is a non-contact, non-invasive, and non-ionizing passive approach. Infrared imaging of the diabetic foot is still mostly reliant on 2D images that only show a portion of the anatomy. In this scenario, a 3D thermal model would allow for better observation and inspection of the region of interest, which includes the plantar, lateral, and dorsal areas. The use of 3D modeling for the diagnosis of the diabetic foot has been documented in a few articles at the publication of this thesis.The proposed method employs a series of merged infrared and visible spectrum images as data input for the 3D point cloud estimation and surface reconstruction, based on Structure from Motion and Multi-view Stereo methods. However, segmentation in thermal images is a task that remains manually performed since the detection of descriptive features is almost impossible in false-color images. Therefore, this thesis presents an automatic segmentation method based on the processing of radiometric information before generating a false-color image. Radiometric data processing is an alternative to digital image processing due to the feasibility to remove thermal interferences (e.g. lamp, thermal shadows, or even patient body parts) based on temperature threshold criteria, improving color contrast, and segmenting the region of interest, and combine onto visible spectrum images.The fused multimodal images were used as input information for the estimation of the 3D surface of the foot. The obtained model was provided with a temperature scale related to the radiometric data obtained by each volunteer, as well as the possibility to rotate the model to observe each viewpoint. The findings show that the 3D multimodal model is feasible, allowing for better and faster visualization of temperature distribution during diabetic foot diagnosis. The contribution of this thesis concerns the acquisition of a 3D model with thermal information and automatic segmentation in thermal images for multimodal fusion. The perspective is the clinical validation to pilot test the assistance in the diagnosis of diabetic foot. However, from the experimental/theoretical perspective, it is contemplated to study the accuracy of image registration with the proposed method of automatic segmentation, and the thermal and spatial accuracy of the 3D models carried out with phantoms