A dual wave infrared imaging polarimeter

A long standing debate has existed on the utility of Mid Wave Infrared (MWIR) vs Long Wave Infrared (LWIR) remote-sensing imagery for Precision Agriculture applications. We have conducted a combined qualitative and quantitative, multi-field, aircraft remote-sensing study to show that LWIR measurements often result in more actionable for day-to-day farm management applications. LWIR imagery when processed with SaraniaSat's nonlinear, weak-signal detection algorithms is an effective predictor of crops stress and ultimate harvest yield. It is also shown that both processed LWIR and MWIR imagery contain actionable, crop-stress information that is absent in the conventional Normalized Difference Vegetation Index (NDVI) that is currently, widely used for Precision Agriculture. Finally, at a simulated 10m ground resolution (satellite remote sensing) it is shown, once again, that LWIR is superior and more actionable.

Multispectral infrared imaging systems that simultaneously operate in the long-wave infrared (LWIR) and mid-wave infrared (MWIR) bands offer significant advantages for target detection and recognition. However, conventional infrared optical systems rely on bulky multi-element lens assemblies to accommodate incident wavelengths of LWIR and MWIR bands, making it challenging for compact thermal optics design. Here, we propose and experimentally demonstrate an inverse designed infrared metalens capable of simultaneously focusing LWIR and MWIR radiation at wavelengths of 9.5 μm and 4.75 μm with a focal length variation of 1%. In the experiment, the proposed metalens with detector enables a dual-wavelength thermal imaging with a compact size (26 × 26 × 18 mm3) and a light weight (19 g). This work establishes a robust and scalable inverse design strategy for dual-wavelength infrared metalenses and provides a promising route toward compact, integrated, and multifunctional infrared imaging lens.

Current stand-off hyperspectral imaging detection solutions that operate in the mid-wave infrared (MWIR), nominally 2.5 – 5 μm spectral region, are limited by the number of absorption bands that can be addressed. This issue is most apparent when evaluating a scene with multiple absorbers with overlapping spectral features making accurate material identification challenging. This limitation can be overcome by moving to the long wave IR (LWIR) region, which is rich in characteristic absorption features, which can provide ample molecular information in order to perform presumptive identification relative to a spectral library. This work utilises an instrument platform to perform negative contrast imaging using a novel LWIR optical parametric oscillator (OPO) as the source. The OPO offers continuous tuning in the region 5.5 – 9.5 μm, which includes a number of molecular vibrations associated with the target material compositions. Scanning the scene of interest whilst sweeping the wavelength of the OPO emission will highlight the presence of a suspect material and by analysing the resulting absorption spectrum, presumptive identification is possible. This work presents a selection of initial results using the LWIR hyperspectral imaging platform on a range of white powder materials to highlight the benefit operating in the LWIR region compared to the MWIR.

The increasing availability of complex multivariate data yielded by sensor technologies permits qualitative and quantitative data analysis for material characterization. Multivariate data are hard to understand by visual inspection and intuition. Thus, data-driven models are required to derive study-specific insights from large datasets. In the present study, a partial least squares regression (PLSR) model was used for the prediction of elemental concentrations using the mineralogical techniques mid-wave infrared (MWIR) and long-wave infrared (LWIR) combined with data fusion approaches. In achieving the study objectives, the usability of the individual MWIR and LWIR datasets for the prediction of the concentration of elements in a polymetallic sulphide deposit was assessed, and the results were compared with the outputs of low- and mid-level data fusion methods. Prior to low-level data fusion implementation, data filtering techniques were applied to the MWIR and LWIR datasets. The pre-processed data were concatenated and a PLSR model was developed using the fused data. The mid-level data fusion was implemented by extracting features using principal component analysis (PCA) scores. As the models were applied to the MWIR, LWIR, and fused datasets, an improved prediction was achieved using the low-level data fusion approach. Overall, the acquired results indicate that the MWIR data can be used to reliably predict a combined Pb–Zn concentration, whereas LWIR data has a good correlation with the Fe concentration. The proposed approach could be extended for generating indicative element concentrations in polymetallic sulphide deposits in real-time using infrared reflectance data. Thus, it is beneficial in providing elemental concentration insights in mining operations.

Real-time small infrared (IR) target detection is critical to the performance of the situational awareness system in high-altitude aircraft. However, current IR target detection systems are generally hardware-unfriendly and have difficulty in achieving a robust performance in datasets with clouds occupying a large proportion of the image background. In this paper, we present new results by using an efficient method that extracts the candidate targets in the pre-processing stage and fuses the local scale, blob-based contrast map and gradient map in the detection stage. We also developed mid-wave infrared (MWIR) and long-wave infrared (LWIR) cameras for data collection experiments and algorithm evaluations. Experimental results using both publicly available datasets and image sequences acquired by our cameras clearly demonstrated that the proposed method achieves high detection accuracy with the mean AUC being at least 22.3% higher than comparable methods, and the computational cost beating the other methods by a large margin.

Target detection and identification are well-studied problems in the visible and near infrared (IR) bands, with recent work focusing on the short wave IR (SWIR) band. The extended SWIR (eSWIR) band (2 to 2.5 μm) offers an advantage over SWIR due to increased atmospheric transmission, while keeping greater diffraction-limited angular resolution than the midwave IR and longwave IR. eSWIR should additionally improve object-sky contrast due to having lower background sky path radiance than the SWIR. An analysis of the signal-to-noise ratio and contrast for drone imaging in the reflective bands is presented and compared with a Night Vision Integrated Performance Model of drone detection performance using equivalent reflectivities. We find that imaging performance across all four bands is strongly dependent on pixel pitch and contrast.

Biomimetic staring infrared imaging omnidirectional detection technology

We present a series of long-wave-infrared (LWIR) polarimetric-based thermal images of facial profiles in which polarization-state information of the image forming radiance is retained and displayed. The resultant polarimetric images show enhanced facial features, additional texture, and details that are not present in the corresponding conventional thermal imagery. It has been generally thought that conventional thermal imagery (MidiR or LWIR) could not produce the detailed spatial information required for reliable human identification due to the so-called ghosting effect often seen in thermal imagery of human subjects. By using polarimetric information, we are able to extract subtle surface features of the human face, thus improving subject identification. The considered polarimetric image sets include the conventional thermal intensity image, S0 , the two Stokes images, S1 and S2, and a Stokes image product called the degree-of-linear-polarization (DoLP) image. Finally, Stokes imagery is combined with Fresnel relations to extract additional 3D surface information.

The high thermal conductivity of silicon has limited its use in thermal radiative devices where minimizing conductive cooling is a priority. Recent advancements in phononic MEMS technology enable a significant reduction in the thermal conductivity of electrically conductive nanowires, achieving extreme thermal isolation for semiconductor applications. This technology can lower thermal conductivity to a nanoWatt/K level approaching the dielectric limit. The nanowires are configured as phononic crystals which increase Bragg scattering of phonons, providing an extreme level of thermal isolation for application devices.
This paper reports for the first time a highly efficient silicon broadband infrared light source based on phononic MEMS technology with emission in the near infrared (NIR) - midwave infrared (MWIR) – longwave infrared (LWIR) wavelength range. The thermal light source is fabricated from a starting silicon-on-insulator (SOI) wafer wherein a heated micro-platform and supporting nanowires are released from the surrounding substrate by removing the underlying SiO2 layer. The nanowires are configured with phononic crystal structure.
The infrared source is an array of silicon pixels packaged within a vacuum cavity providing broadband infrared NIR-MWIR-LWIR radiation from micro-platforms. The device is called a light-emitting platform (LEP), which differentiates it from a narrowband LED emitter.
LEP pixel radiative power efficiency within the vacuum cavity is over 90% with micro-platform temperature as high as 800 ˚C for silicon. The LEP efficiency compares with an Edison incandescent lightbulb at less than 10% for RGB light and 2.5% for uncooled LEDs emitting in the 2.3 µm wavelength range. Applications include MWIR floodlights, spotlights, vehicle headlights, biomedical stimulators, instrumentation light sources, etc.

We present a new optical system for infrared (IR) image-spectrum integration remote sensing. The purpose to develop this instrument is to find the key spectral characteristics of typical hot target and to explore a new intelligence fusion method for the recognition. When mounted on a two-dimensional rotation stage, it can track the suspected target by image processing, and then get its spectrum to do recognition. It is a dual-band system with long-wave infrared (LWIR) imaging and mid-wave infrared (MWIR) spectrum. An IR dichroic beamsplitter is used to divide wideband incident infrared into LWIR and MWIR. Compared to traditional infrared combined imaging and spectral-analysis instruments, it yields higher sensitivity for measuring the IR spectrum. The sensors for imaging and spectrum detection are separate, so high spatial resolution, frame rate, and spectrum resolution can all be obtained simultaneously.

Optical design of athermalized infrared dual-band annular folded lens with multilayer imaging diffractive optical elements

The development of a scalable, low cost, low power, and room temperature operating detector technology capable of high spatial resolution imaging over IR bands of interest can advance and extend space and satellite sensing capabilities such as remote sensing and earth observation. Conventional infrared (IR) band photodetectors based on HgCdTe material operating over short-wave infrared (SWIR), mid-wave infrared (MWIR), and/or long-wave infrared (LWIR) bands often require external cooling to achieve high performance IR sensing and imaging. By integrating bilayers of doped graphene to function as a high mobility channel to aid the recombination of photogenerated carriers, a graphene-enhanced IR photodetector with HgCdTe absorbing layer can be developed providing high performance uncooled detection over SWIR, MWIR, and LWIR (~1.3-14 μm) bands. Development of the high-performance IR photodetector technology involves ptype doping of graphene bilayers deposited on Si/SiO₂ with boron using a spin-on dopant (SOD) process, and then transfer of the doped bilayer graphene onto HgCdTe substrates. The graphene-enhanced HgCdTe SWIR/MWIR/LWIR band detectors were analyzed and characterized material and optoelectronically to demonstrate high performance IR detection at room temperature for advanced NASA Earth Science, defense, and commercial applications.

Since 2002, the THALES Group has been manufacturing sensitive arrays using QWIP technology based on GaAs and related III-V compounds, at THALES Research and Technology Laboratory. The QWIP technology allows the realization of large staring arrays for Thermal Imagers (TI) working in the long-wave infrared (LWIR) band (8-12 μm). In the past researchers claimed many advantages of QWIPs. Uniformity was one of these and has been the key parameter for the production to start. The 640x512 LWIR focal plane arrays (FPAs) with 20μm pitch was the demonstration that state of the art performances can be achieved even with small pixels. This opened the field for the realization of usable and affordable megapixel FPAs. Thales Research & Technology (TRT) has been developing third generation GaAs LWIR QWIP arrays for volume manufacture of high performance low cost thermal imaging cameras. In the past, another widely claimed advantage for QWIPs was the so-called band-gap engineering and versatility of the III-V processing allowing the custom design of quantum structures to fulfil the requirements of specific applications such as very long wavelength (VLWIR) or multispectral detection. In this presentation, we present the performances of both our first 384x288, 25 μm pitch, MWIR (3-5μm) / LWIR (8-9 μm) dual-band FPAs, and the current status of QWIPs for MWIR ( 15μm) arrays.

An infrared (IR) sensor model is validated using experimentally derived peak pixel signal-to-noise ratio (SNR) versus range for detection of either an unresolved or a resolved unmanned aerial vehicle (UAV) target. The model provided estimates the time-averaged peak SNR values for the ranges used in the field collection. A mid-wave infrared (MWIR) camera and a long-wave infrared (LWIR) camera provided the measured data. Commercially available UAVs are flown along a line from the cameras to a clear sky region for background. A laser range finder measures the range at seven stopping points along the path. The data result in five ranges of unresolved target information for the MWIR camera and in four ranges for the LWIR camera. We provide details for using the data collected from the model to match the cameras used in the field collection. Also, the processing used to extract peak SNR versus range from imagery is presented.

The mid- and long-wave infrared point spectrometer (MLPS) is an infrared point spectrometer that utilizes unique technologies to meet the spectral coverage, spectral sampling, and field-of-view (FOV) requirements of many future space-borne missions in a small volume with modest power consumption. MLPS simultaneously acquires high resolution mid-wave infrared (∼2-4 µm) and long-wave infrared (∼5.5-11 µm) measurements from a single, integrated instrument. The broadband response of MLPS can measure spectroscopically resolved reflected and thermally emitted radiation from a wide range of targets and return compositional, mineralogic, and thermophysical science from a single data set. We have built a prototype MLPS and performed end-to-end testing under vacuum showing that the measured spectral response and the signal-to-noise ratio (SNR) for both the mid-wave infrared (MIR) and long-wave infrared (LIR) channels of MLPS agree with established instrument models.