Infrared Line Scanner Research Articles

Development and validation of a 64-channel ROIC prototype for SWIR line scan sensor applications

This paper introduces the development and validation of a 64-channel readout integrated circuit (ROIC) prototype, specifically engineered for short-wave infrared (SWIR) line scan sensors. The design of the prototype undergoes various evaluation through comprehensive silicon-level testing, ensuring its robust performance across a variety of operational modes. Key features such as capacitive transimpedance amplifier (CTIA) gain control and sensitivity control are examined, demonstrating the prototype's ability to handle different input currents and capacitance values with precision. Fabricated with 0.18-μm CMOS technology, the ROIC is tailored for integration with Indium Gallium Arsenide (InGaAs) pixels, facilitating high-resolution imaging. The prototype consumes 26.55 mW with A 3.3 V power supply. The fabricated chip show that the total random noise (RN) level is 128 μVrms and column fixed pattern noise (FPN) is 0.16 mVrms

Neural Network-Based Investigation of Periodic Noise Reduction Methods for High-Resolution Infrared Line Scanning Images

In the realm of neural network-based noise reduction, conventional models predominantly address Gaussian and blur artifacts across entire images. However, they encounter notable challenges when directly applied to periodic noise characteristics of high-resolution infrared sequential imagery. The high resolution also complicates the construction of suitable datasets. Our study introduces an innovative strategy that transforms two-dimensional images into one-dimensional signals, eliminating the need for processing the full image. We have developed a simulated dataset that closely mirrors natural infrared line scanning images derived from the FLIR dataset. To address low-frequency periodic noise, we propose two neural-network-based denoising approaches. The first employs a neural network to deduce noise from the one-dimensional signal, while the second utilizes discrete Fourier transforms for noise prediction within the frequency domain. Our experimental results highlight the Restormer model’s exemplary performance in direct noise prediction, where denoised images attain a PSNR of around 41 and an SSIM close to 0.9 on simulated data, leaving minimal residual noise in the actual denoised images. Further, we investigate the influence of Fourier coefficients, as predicted by neural networks, on the denoising process in the second approach. Employing 12 frequency coefficients, the Restormer and NAFNet models both reach a PSNR near 34 and an SSIM of approximately 0.842.

Pattern recognition analysis of marine oil spills in airborne passive infrared multispectral remote sensing images.

Pattern recognition methodology was developed for the automated detection of marine oil spills in passive infrared multispectral remote sensing images. The images employed in this work were collected from the Deepwater Horizon oil spill accident in 2010. The imaging instrument for data collection was a downward-looking infrared line scanner equipped with eight optical bandpass filters in the spectral range of 8-12 μm on a fixed-wing aircraft. Oil slicks may show either positive or negative thermal contrast against the surrounding sea water, depending on the sun glint conditions or the oil thickness. Classifiers were developed separately to detect oil with different contrasts by the application of backpropagation neural networks to the preprocessed radiances. Preprocessing strategies included: (1) assembly of training data through k-means clustering analysis; (2) elimination of variation in radiance magnitudes by a customized temperature correction method; (3) removal of sun glint artifacts in images by polynomial correction; and (4) extraction of the most representative features as inputs for the neural networks by a subset selection approach. The classifiers designed to detect oil with positive and negative thermal contrast relative to water achieved overall classification accuracies of 88.7 and 92.2%, respectively. Composite classification images were generated by integrating classification scores produced by the two classifiers. The prediction performance of the classification system was demonstrated through its application to images not involved during the training of the networks.

Neural networks for the automated detection of methanol vapour from airborne passive infrared multispectral imaging data

ABSTRACT Pattern recognition methodology was developed for the automated detection of methanol vapour plumes from passive multispectral infrared remote sensing data. The data employed in this work were collected with an infrared line scanner equipped with eight spectral bandpass filters mounted in a downward-looking mode on a fixed-wing aircraft. An automated classifier was developed by the application of a backpropagation neural network to the calibrated, registered, and preprocessed radiances. Preprocessing steps were performed to optimize the inputs for the neural network, which included: (1) contrast enhancement by calculating the ratios of band intensities; (2) assembly of training data by use of the -means clustering algorithm; (3) removal of temperature information from the measured radiance data; (4) feature extraction for identifying the most representative ratios of band intensities; and (5) optimization of the starting assumed emissivity value for the temperature and emissivity separation algorithm. The best classifier achieved an overall classification accuracy of 98.07% on the testing set with a false detection rate of 0.90% and a missed detection rate of 13.50%. The prediction performance of the optimized neural network was demonstrated through its application to 16 images not included in the training procedure.

A registration based nonuniformity correction algorithm for infrared line scanner

A scene-based algorithm is developed for nonuniformity correction in focal plane of line scanning infrared imaging systems (LSIR) based on registration. By utilizing the 2D shift between consecutive frames, an implicit scheme is proposed to determine correction coefficients. All nonuniform biases are corrected to the same designated value, without estimating and removing biases explicitly, permitting quick computation for high-quality nonuniformity correction. Firstly, scene motion is estimated by image registration and consecutive frames exhibiting required 2D subpixel shift are collected. Secondly, we retrieve the difference matrix of adjacent biases by utilizing the 2D shift between consecutive frames. Thirdly, we perform specified elementary transformations and corresponding cumulative sums to the difference matrix to obtain a bias compensator. This bias compensator converts nonuniform biases to a designated detector’s bias. Finally, based on the different bias compensators obtained from several frame pairs, we calculate an averaged bias compensator for nonuniformity correction with less error. Quantitative comparisons with other nonuniformity correction methods demonstrate that the proposed algorithm achieves better fixed-pattern noise reduction with low computational complexity.

Influence of mineral particle size and choice of suitable parameters for ore sorting using near infrared sensors

Near infrared sensor-based sorting is an emerging preconcentration technology which holds promise for many mineral processing applications, such as elimination of calcite and clay waste from ore. Preconcentration serves to increase the processing efficiency as well as to reduce the total processing cost through the rejection of unwanted gangue. Given small-scale heterogeneity in complex ores, i.e. assorted minerals occurring side-by-side inside an area of measurement, the total mineral composition may not be easily discerned from a near infrared spectrum. Hence, mineral identification and subsequent classification involves the analysis of absorption features in terms of feature depth, width, position, and level of spectral reflectance. This research investigates the near infrared spectral characteristics of minerals as a function of particle size fraction, specifically for individual minerals commonly found in malachite-rich copper ores. Samples of pure minerals are crushed and sieved into different size fractions and scanned with a near infrared line scanner. It was found that the presence of characteristic absorption features and their wavelength position were a better identification parameter than the reflectance level. The implication for preconcentrating a typical malachite-rich copper ore through near-infrared based sorting is discussed.

Temperature Measurement Using the Wedge Method: Comparison and Application to Emissivity Estimation and Compensation

Temperature measurement based on infrared radiation depends on correctly adjusted emissivity. However, emissivity configuration is complex as emissivity is not normally known with precision; it is influenced by radiation reflections and can also vary with the temperature. The wedge method is a temperature measurement method which assures the selection of the correct emissivity configuration using an infrared camera to take images of a wedge region. Within the wedge, a virtually closed cavity for radiation is created. Although this method outperforms traditional infrared measurement, no comparison has yet been carried out under real industrial conditions which would provide information about emissivity variations. This paper proposes a method to measure temperature using the wedge method in industrial environments and compares the results with the temperature measurement acquired from a calibrated infrared line scanner. Using the wedge method, it is possible to accurately estimate emissivity profiles under real working conditions. A method to apply these profiles for emissivity compensation is also proposed in this paper. Conclusions give the analysis of the strengths and weaknesses of the two methods and provide recommendations and guidelines for technicians interested in temperature measurement and emissivity estimation and compensation.

Simulated Radiance Profiles for Automating the Interpretation of Airborne Passive Multi-Spectral Infrared Images

Methodology is developed for simulating the radiance profiles acquired from airborne passive multispectral infrared imaging measurements of ground sources of volatile organic compounds (VOCs). The simulation model allows the superposition of pure-component laboratory spectra of VOCs onto spectral backgrounds that simulate those acquired during field measurements conducted with a downward-looking infrared line scanner mounted on an aircraft flying at an altitude of 2000-3000 ft (approximately 600-900 m). Wavelength selectivity in the line scanner is accomplished through the use of a multichannel Hg:Cd:Te detector with up to 16 integrated optical filters. These filters allow the detection of absorption and emission signatures of VOCs superimposed on the upwelling infrared background radiance within the instrumental field of view (FOV). By combining simulated radiance profiles containing analyte signatures with field-collected background signatures, supervised pattern recognition methods can be employed to train automated classifiers for use in detecting the signatures of VOCs during field measurements. The targeted application for this methodology is the use of the imaging system to detect releases of VOCs during emergency response scenarios. In the work described here, the simulation model is combined with piecewise linear discriminant analysis to build automated classifiers for detecting ethanol and methanol. Field data collected during controlled releases of ethanol, as well as during a methanol release from an industrial facility, are used to evaluate the methodology.

Pragmatic Genetic Programming strategy for the problem of vehicle detection in airborne reconnaissance

A Genetic Programming (GP) method uses multiple runs, data decomposition stages, to evolve a hierarchical set of vehicle detectors for the automated inspection of infrared line scan imagery that has been obtained by a low flying aircraft. The performance on the scheme using two different sets of GP terminals (all are rotationally invariant statistics of pixel data) is compared on 10 images. The discrete Fourier transform set is found to be marginally superior to the simpler statistics set that includes an edge detector. An analysis of detector formulae provides insight on vehicle detection principles. In addition, a promising family of algorithms that take advantage of the GP method’s ability to prescribe an advantageous solution architecture is developed as a post-processor. These algorithms selectively reduce false alarms by exploring context, and determine the amount of contextual information that is required for this task.

Automated Detection of Chemical Vapors by Pattern Recognition Analysis of Passive Multispectral Infrared Remote Sensing Imaging Data

Pattern recognition methods are developed for the automated interpretation of passive multispectral imaging data collected from an airborne platform. Through the use of an infrared line scanner equipped with 14 spectral bandpass filters, passive infrared images are collected of an ammonia plant within a nitrogen fertilizer facility. Piecewise linear discriminant analysis is used to implement an automated algorithm for the detection of scene pixels that correspond to chemical vapor signatures. A separate classifier is used to detect the presence of hot carbon dioxide (CO2) within the images. In the assembly of training and prediction data for the development of both classifiers, the K-means clustering algorithm is used together with knowledge of the site to assign pixels to the plume/nonplume and CO2/non-CO2 categories. The effects of temperature variation within the imaged scene are removed from the data through the use of an algorithm for separating the contributions of temperature and emissivity to the Planck equation. Averaged across four data runs containing a total of 3.5 million pixels, the resulting discriminants are observed to detect approximately 91% of the plume pixels while achieving a false detection rate of less than 0.01%. The corresponding performance criteria for the CO2 classifier are a successful detection of approximately 94% of the pixels with a CO2 signature and a false detection rate of less than 0.7%. The robustness of the CO2 classifier is further enhanced through the adoption of a probability-based classification rule.

A thermal nondestructive evaluation system for detecting vertical cracks in unidirectional carbon fiber composites

A thermal system for nondestructive detection of broken fibers in unidirectional carbon fiber composites is described. The sample is heated inductively so that a line-shaped coil is scanned in the direction of the fibers. Since the thermal conductivity along the fibers is much better than in the perpendicular direction, the heat generated flows mainly in the scanning direction. Thus, the broken fibers hinder the heat flow effectively. They can be detected by monitoring the thermal radiation from the sample surface using an infrared line scanner, the image line of which is synchronized with the heating line in order to record a two-dimensional thermal image. The effect of broken fibers on the surface temperature of unidirectional carbon fiber composites is evaluated numerically and the feasibility of the system is verified experimentally.

Linear model of infrared line scanner imaging using wave optics approach

Infrared line scanners (IRLS) have been modelled on the basis of linear filter theory. Imaging through a scanner objective is described with wave optics. Effective wavelength and effective radiance are introduced to reduce the dependence on wavelength. Our theoretical predictions of IRLS images agree well with experimental IRLS images.

An infrared line scanning technique for detecting delaminations in carbon fibre tubes

A thermal nondestructive testing method based on an infrared line scanner for cylindrical specimens is presented. A seven-layered carbon-glass fibre tube with an artificial delamination was used as a test sample. A finite difference computational model of the heating of the tube was constructed in order to simulate the measurement and evaluate the capability of detecting delaminations. The numerical results show the temperature difference caused by the delamination to be well above the practical detection limit, which was verified by the actual measurements.

Transportable Infrared Line Scanner Based Equipment for Thermal Nondestructive Testing

Transportable Infrared Line Scanner Based Equipment for Thermal Nondestructive Testing

Transportable Infrared Line Scanner Based Equipment for Thermal Nondestructive Testing

Transportable Infrared Line Scanner Based Equipment for Thermal Nondestructive Testing

Transportable infrared line scanner based equipment for thermal nondestructive testing

Abstract Transportable equipment for on-site thermal nondestructive testing is described. The equipment is constructed from two separate units: a hand-held infrared line scanner and a remote unit containing all the necessary control devices. The sample surface is heated by manually scanning a line source over the surface and the resulting surface temperature rise is simultaneously measured with the line scanner. The heating line is generated either by a line-focused laser beam or by a radio frequency induction coil. The obtained surface temperature distribution of the monitored area is presented as a pseudo-color image. The feasibility of the method is demonstrated with a carbon-glass fiber composite sample.

Fast temperature measurement by infra-red line scanning in a hypersonic shock tunnel

Fast temperature measurement by infra-red line scanning in a hypersonic shock tunnel

Objective and Subjective Assessment of Image Recognition

This paper describes two studies which used objective and subjective assessments to quantify the effect of target degradation on observers' recognition ability. ‘Noise’ inherent in a digital infra-red line scan system can result in a static line-to-line variation (pixel jitter) over the displayed imagery. The amount of target degradation is dependent upon both the amplitude and frequency of the pixel jitter. The results showed that, firstly, if an image is affected by pixel jitter, even with an amplitude of only 1 pixels, a significant interference in target recognition performance occurs. Secondly, the results from the subjective scaling mirrored closely the error data and therefore imply that this rating scale, may have widespread utility in target acquisition studies. Finally, the effect of pixel jitter appears to be robust. The effect was found not to be specific to a particular type of imagery and is, therefore, likely to generalise to other types of target and other imaging systems. The implication of these results for user-system specification is discussed.

Observation and study on the dark defects in InGaAsP/InP double-heterostructure leds

The defects in InGaAsP/InP DH LEDs are observed with an infrared line scanner. The dark structure appears before aging and it exists mainly in the form of dark spot defect. The effect of the variety and concentration of the doping forp-InP confining layers on the dark defects is studied. The results show that the percentage of devices with dark defects is much lower for Mg or In−Zn doped devices than for Zn doped devices. It is believed that Zn is one of the important origin for the formation of dark defects. The growth rate of dark defects is studied both at room temperature and at 70–85°C. The results show that after agin for 15000 h at room temperature there are no dark defects newly appeared. But after aging for 2000 h at 70–85°C some devices show newly formed dark structure with very slow growth rate.

Fluid flow in crystalline rocks: Relationships between groundwater spring alignments and other surface lineations at Altnabreac, United Kingdom

The Strath Halladale Granite in the region around Altnabreac, northern Scotland, United Kingdom, has been studied with a view to establishing a relationship between the regional distribution of faults and fracture zones, surface discharges of groundwater, and groundwater flow systems. A major component of the groundwater flow is through the rock fractures. Because of the extensive superficial cover the surface expression of major fractures was difficult to identify from the limited surface exposures. Geophysical surveys and aerial photography enabled the authors to define lineations which could be related to the presence of fractures. The areal distribution of groundwater spring discharges was mapped using thermal infrared line scan techniques. The distribution of these springs has been studied to assess their relationships to surface lineaments and to correlations with geophysical and fracture mapping data.