Long Wave Infrared Sensor Research Articles

Mid-wave and long-wave infrared signature model and measurement of power lines against atmospheric path radiance

The signal to noise ratio and corresponding visibility of power cables as seen by military aircrafts is critical for crew safety. During low altitude operations, rotorcraft systems must be able to navigate these power lines during flight. Many of these military missions are flown at night which means the reflective bands including the visible, near infrared and short-wave infrared do not provide sufficient light. However, the emissive bands of the mid-wave infrared (MWIR) and long-wave infrared (LWIR) can be used to distinguish the location of these wires. LWIR sensors are typically used for pilotage applications. In both the LWIR and MWIR, the signal to noise depends on the wire emissivity and reflectivity as well as the ground and sky background path radiance. The signal to noise ratio is strongly dependent on the elevation of the viewing angle. In this paper, we model the signal to noise ratio as a function of elevation viewing angle using wire reflectivity and emissivity as well as MODTRAN calculations for path radiance. We also take MWIR and LWIR measurements to compare these two bands to the modelling results. We provide a summary of both model and measurements and make conclusions.

Simple target acquisition model based on Fλ/d

The US Army Night Vision and Electronic Sensors Directorate’s Night Vision Integrated Performance Model (NVIPM) is a robust, comprehensive, and sophisticated model that calculates the performance of an infrared imager in the task of target acquisition. The inputs are specifics about the target, atmosphere, optics, detector, electronics, and display and the outputs are probabilities of target detection, recognition, and identification. NVIPM is complicated and takes a good bit of time to master obtaining correct answers based on numerous assumptions and modeling experience. For midwave infrared (MWIR) and longwave infrared (LWIR) sensors, past research has shown that imaging system performance is strongly related to Fλ / d, where F is the f-number, λ is the wavelength, and d is the detector pitch. Fλ / d provides a metric that relates how closely to diffraction-limited performance a sensor operates. We use the past Fλ / d work to develop a simple model that provides probability of target discrimination as a function of range that can be performed with a simple hand calculator or spreadsheet. We compare this model to the NVIPM calculations on 10 very disparate MWIR and LWIR sensors to show robust agreement. We also describe the conditions under which the simple model is valid.

DETERMINING MORPHOMETRIC PROPERTIES OF RADIATA PINE USING LONG WAVE INFRARED SENSING AND BIOLOGICALLY-INSPIRED VISION

Abstract. A miniaturised, high resolution visible and long wave infrared (LWIR) sensor was carried onboard an unmanned aerial vehicle to observe sections of radiata pine forests. The raw irradiance measurements were temporally pre-processed using a biologically-inspired vision (BIV) model to allow information within and across the images to be normalised. This permitted a larger, denser, and more tailored set of key points within the 2D image stacks to be corresponded, thereby improving 3D reconstructions of individual trees derived using structure from motion (SfM). The BIV model comprises multiple layers of processing derived from measured or assumed responses of the photoreceptor cells in the hoverfly. Its pre-processing expands the range of input signal obtained from the LWIR sensor and enhances foreground-background contrast. Morphological image processing techniques were also applied to enhance key image features before structure from motion is applied. The result allows structural properties of individual trees to be characterised in terms of their potential volume and quality; and contrasted with the point clouds obtained from the visible imagery that only depicts the tree canopies.

Mapping Surface Quartz Content in Sand Dunes Covered by Biological Soil Crusts Using Airborne Hyperspectral Images in the Longwave Infrared Region

Biological soil crusts (BSCs), composed of cyanobacteria, algae, mosses, lichens, and fungi, are important ecosystem engineers that stabilize the quartz-rich dunes in the Nitzana study area near the Israel–Egypt border. The longwave infrared (LWIR) region of the electromagnetic spectrum is very useful for quartz identification since quartz reflectance in the visible, near infrared, and shortwave infrared (VIS-NIR-SWIR, 0.4–2.5 µm) spectral regions lacks identifying features, whereas in the LWIR region, the quartz emissivity spectrum presents a strong doublet feature. This emissivity feature can be used as a diagnostic tool for BSCs development in desert environments, because BSCs attenuate the quartz feature as a function of their successional development. A pair of day and night airborne hyperspectral images were acquired using the Specim AisaOWL LWIR sensor (7.7–12 µm) and processed using an innovative algorithm to reduce the atmospheric interference in this spectral domain. The resulting day and night apparent emissivity products were used to produce a surface quartz content map of the study area. The significant reduction in atmospheric interference resulted in a high correlation (R2 = 0.88) between quartz content in field samples determined by X-ray powder diffraction analysis and emissivity estimations from the airborne images. This, in turn, served as the ground truth to our quartz content map of the surface, and by proxy to the BSC.

Search and Rescue Operations with an Unmanned Helicopter

We tested the performance of both electro-optical (EO) and long-wave infrared (LWIR) video sensors on an unmanned helicopter in a simulated Search and Rescue (SAR) setting. Our objectives were to examine whether humans and other objects associated with (SAR) operations could be reliably observed in the video sensor imagery in direct light and shade. Our unmanned helicopter flight occurred in a forest in the U.S. Pacific Northwest under a Certificate of Authorization (COA) issued by the Federal Aviation Association (FAA). Analysis of the EO and LWIR video imagery from several hover positions determined that many of the SAR objects could be reliably detected. Detection success was heavily influenced by factors such as distance, object size, material and color. In direct sun light, the EO sensor performed more reliably than the LWIR sensor in detecting most objects. The LWIR sensor, however, appeared to be preferable to the EO sensor when tracking human subjects in shady environments. We believe unmanned aircraft can offer SAR operations flexibility in reaching areas that might endanger manned flight crews. In addition, unmanned aircraft also have the capability to fly closer to the ground, and at slower speeds than some manned aircraft, which potentially leads to greater image resolution and ultimately increases detection success.

Infrared detector size: how low should you go?

In the past five years, significant progress has been accomplished in the reduction of infrared detector pitch and detector size. Recently, longwave infrared (LWIR) detectors in limited quantities have been fabricated with a detector pitch of 5 μm. Detectors with 12-μm pitch are now becoming standard in both midwave infrared (MWIR) and LWIR sensors. Persistent surveillance systems are pursuing 10-μm detector pitch in large format arrays. The fundamental question that most system designers and detector developers desire an answer to is: "How small can you produce an infrared detector and still provide value in performance?" If a system is mostly diffraction-limited, then developing a smaller detector is of limited benefit. If a detector is so small that it does not collect enough photons to produce a good image, then a smaller detector is not much benefit. Resolution and signal-to-noise are the primary characteristics of an imaging system that contribute to targeting, pilotage, search, and other human warfighting task performance. We investigate the task of target discrimination range performance as a function of detector size/pitch. Results for LWIR and MWIR detectors are provided and depend on a large number of assumptions that are reasonable.

Probability of identification of small hand-held objects for electro-optic forward-looking infrared systems

This paper describes research on the measurement of the 50% probability of identification cycle criteria (N50,V50) for a set of hand-held objects normally held or used in a single hand. These cycle criteria are used to calibrate the Night Vision Electronic Sensors Directorate (NVESD) target acquisition models. The target set consists of 12 objects, from innocuous to potentially lethal. Objects are imaged in the visible, midwave infrared (MWIR), and long-wave infrared (LWIR) spectrum at 12 different aspects. Two human perception experiments are performed. The first experiment simulates an incremental constriction of the imaging systems modulation transfer function (MTF). The N50, and V50 calibration criteria are measured from this perception experiment. The second experiment not only simulates an incremental constriction of the system MTF but also down samples the imagery to simulate the objects at various ranges. The N50 and V50 values are used in NVTherm 2002 and NVThermIP, respectively, to generate range prediction curves for both the LWIR and MWIR sensors. The range predictions from both NVTherm versions are then compared with the observer results from the second perception experiment. The comparison between the results of the second experiment and the model predictions provides a verification of measured cycle criteria.