General Image-Quality Equation: GIQE

Radiometry and contrast-to-noise ratio for continuous-wave and laser range-gated active imaging systems

Resolution and sensitivity must be considered in the design of an active imaging system. System sensitivity is characterized by the signal-to-noise or contrast-to-noise ratio and is derived through radiometry. We present a tutorial for the radiometry associated with the contrast-to-noise ratio for active continuous-wave and laser range-gated imaging systems, giving a useful metric for determining reflective-band sensor performance against a target and background. A calculation of the full power and contrast-to-noise ratio terms is shown for an example case, and all relevant radiometric signal terms are covered while describing the assumptions made. Coherent effects on signal-to-noise ratio are excluded from this analysis.

Drones for litter monitoring on coasts and rivers: suitable flight altitude and image resolution

Multirotor drones can be efficiently used to monitor macro-litter in coastal and riverine environments. Litter on beaches, dunes and riverbanks, along with floating litter on coastal and river waters, can be spotted and mapped from aerial drone images. Items detection and classification are prone to image resolution, which is expressed in terms of Ground Sampling Distance (GSD). The GSD is determined by drone flight altitude and camera properties. This paper investigates what is a suitable GSD value for litter survey. Drone flight altitude and camera setup should be chosen to obtain a GSD between 0.5 cm/px and 1.25 cm/px. Within this range, the lowest GSD allows litter categorization and classification, whereas the highest value should be adopted for a coarser litter census. In the vision of drawing up a global protocol for drone-based litter surveys, this work sets the ground for homogenizing data collection and litter assessments.

Spatial aliasing quantification and sampling frequency selection in imaging sensors

Sampling, whether it be spatial or temporal, is a common occurrence. A result of this fact is the need for an anti-aliasing filter, which effectively limits high frequencies and prevents them from folding over and appearing as a low(er) frequency when sampled. In typical imaging sensors, such as optics plus focal plane detector(s), the optical transfer function (OTF) acts as a spatial anti-aliasing filter. However, decreasing this anti-aliasing cutoff frequency (or lowering the curve in general) via the OTF is tantamount to image degradation. On the other hand, the lack of high-frequency attenuation produces aliasing within the image, which is another form of image degradation. In this work, aliasing is quantified, and a method for sampling frequency selection is brought forth.

SkySat Data Quality Assessment within the EDAP Framework

Cal/Val activities within the Earthnet Data Assessment Pilot (EDAP) Project of the European Space Agency (ESA) cover several Earth Observation (EO) satellite sensors, including Third-Party Missions (TPMs). As part of the validation studies of very-high-resolution (VHR) sensor data, the geometric and radiometric quality of the images and the mission compliance of the SkySat satellites owned by Planet were evaluated in this study. The SkySat constellation provides optical images with a nominal spatial resolution of 50 cm, and has the capacity for multiple visits of any place on Earth each day. The evaluations performed over several test sites for the purpose of the EDAP Maturity Matrix generation show that the high resolution requirement is fulfilled with high geometric accuracy, although various systematic and random errors could be observed. The 2D and 3D information extracted from SkySat data conform to the quality expectations for the given resolution, although improvements to the vendor-provided rational polynomial coefficients (RPCs) are essential. The results show that the SkySat constellation is compliant with the specifications and the accuracy results are within the ranges claimed by the vendor. The signal-to-noise ratio assessments revealed that the quality is high, but variations occur between the different sensors.

Volumetric imaging efficiency: the fundamental limit to compactness of imaging systems

A new metric for imaging systems, the volumetric imaging efficiency (VIE), is introduced. It compares the compactness and capacity of an imager against fundamental limits imposed by diffraction. Two models are proposed for this fundamental limit based on an idealized thin-lens and the optical volume required to form diffraction-limited images. The VIE is computed for 2,871 lens designs and plotted as a function of FOV; this quantifies the challenge of creating compact, wide FOV lenses. We identify an empirical limit to the VIE given by VIE < 0.920 × 10^-0.582×FOV when using conventional bulk optics imaging onto a flat sensor. We evaluate VIE for lenses employing curved image surfaces and planar, monochromatic metasurfaces to show that these new optical technologies can surpass the limit of conventional lenses and yield >100x increase in VIE.

Airborne validation of the general image quality equation 5

The general image quality equation (GIQE) maps imaging system parameters to performance in terms of common detection and recognition tasks. Changes to the National Imagery Interpretation Rating Scale led to the development of GIQE version 5 in 2015. The purpose of this paper is to provide a review of GIQE 5 applications in the literature, a tutorial on its use, and, for the first time , an independent validation of the new model along with comparisons to GIQEs 3 and 4.

Designing an incoherent optical detection sensor (LiDAR) utilizing a range-compensating lens

There are many trades to be made when designing an optical system. In this work, an incoherent optical detection sensor (often referred to as an energy- or direct-detection sensor, or a time-of-flight LiDAR) is designed at the sensor or top level using newly developed tools [Appl. Opt.59, 1939 (2020)APOPAI0003-693510.1364/AO.384135]. While incoherent detection sensors, relative to coherent frequency or phase-modulated sensors, are not as useful in cluttered environments, they have their place due to their simplicity and high performance in uncluttered or lightly cluttered environments. In this particular design, a nontraditional receive lens is utilized that has the unique ability to adjust the amount of return signal placed on the detector based on target range, i.e., a range-compensating lens (RCL) [Appl. Opt.58, 7921 (2019)APOPAI0003-693510.1364/AO.58.007921]. Only a two-element RCL is utilized in this work, but it proves the ability to shape the return signal gauging the changes in the stochastic performance, paving the way to a multi-element RCL for additional design freedom in shaping.

Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications

Nanosatellites are increasingly being used in space-related applications to demonstrate and test scientific capability and engineering ingenuity of space-borne instruments and for educational purposes due to their favourable low manufacturing costs, cheaper launch costs, and short development time. The use of CubeSat to demonstrate earth imaging capability has also grown in the last two decades. In 2017, a South African company known as Space Commercial Services launched a low-orbit nanosatellite named nSight-1. The demonstration nanosatellite has three payloads that include a modular designed SCS Gecko imaging payload, FIPEX atmospheric science instrument developed by the University of Dresden and a Radiation mitigation VHDL coding experiment supplied by Nelson Mandela University. The Gecko imager has a swath width of 64 km and captures 30 m spatial resolution images using the red, green, and blue (RGB) spectral bands. The objective of this study was to assess the interpretability of nSight-1 in the spatial dimension using Landsat 8 as a reference and to recommend potential earth observation applications for the mission. A blind image spatial quality evaluator known as Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) was used to compute the image quality for nSight-1 and Landsat 8 imagery in the spatial domain and the National Imagery Interpretability Rating Scale (NIIRS) method to quantify the interpretability of the images. A visual interpretation was used to propose some potential applications for the nSight1 images. The results indicate that Landsat 8 OLI images had significantly higher image quality scores and NIIRS results compared to nSight-1. Landsat 8 has a mean of 19.299 for the image quality score while nSight-1 achieved a mean of 25.873. Landsat 8 had NIIRS mean of 2.345 while nSight-1 had a mean of 1.622. The superior image quality and image interpretability of Landsat could be attributed for the mature optical design on the Landsat 8 satellite that is aimed for operational purposes. Landsat 8 has a GDS of 30-m compared to 32-m on nSight-1. The image degradation resulting from the lossy compression implemented on nSight-1 from 12-bit to 8-bit also has a negative impact on image visual quality and interpretability. Whereas it is evident that Landsat 8 has the better visual quality and NIIRS scores, the results also showed that nSight-1 are still very good if one considers that the categorical ratings consider that images to be of good to excellent quality and a NIIRS mean of 1.6 indicates that the images are interpretable. Our interpretation of the imagery shows that the data has considerable potential for use in geo-visualization and cartographic land use and land cover mapping applications. The image analysis also showed the capability of the nSight-1 sensor to capture features related to structural geology, geomorphology and topography quite prominently.

Back-of-the-envelope image quality estimation using the national image interpretability rating scale

For an optical imaging system, the critical output is the image itself, and therefore, the quality of that image is of utmost importance. To estimate or predict the image quality (IQ), a simulation/model is typically created to yield an output image, given an imaging system and an object/scene. The IQ is typically graded based on the imaging system along with the scene. Developing an imaging simulation and creating input scenes to produce IQ results can be time-consuming, leading to a desire for a simple method to estimate the predicted IQ. This work develops a national image interpretability rating scale (NIIRS) IQ value based on simplifying assumptions for remote-sensing purposes. While the results are on the optimistic side, this back-of-the-envelope IQ estimation allows the process of developing a new imaging system to move forward more in parallel rather than in series, i.e., developing an imaging full simulation/model in parallel with designing/procuring hardware.

Synergistic Effect of Multi-Sensor Data on the Detection of Margalefidinium polykrikoides in the South Sea of Korea

Since 1995, Margalefidinium polykrikoides blooms have occurred frequently in the waters around the Korean peninsula. In the South Sea of Korea (SSK), large-scale M. polykrikoides blooms form offshore and are often transported to the coast, where they gradually accumulate. The objective of this study was to investigate the synergistic effect of multi-sensor data for identifying M. polykrikoides blooms in the SSK from July 2018 to August 2018. We found that the Spectral Shape values calculated from in situ spectra and M. polykrikoides cell abundances in the SSK were highly correlated. Comparing red tide spectra from near-coincident multi-sensor data, remote-sensing reflectance (Rrs) spectra were similar to the spectra of in situ measurements from blue to green wavelengths. Rrs true-color composite images and Spectral Shape images of each sensor showed a clear pattern of M. polykrikoides patches, although there were some limitations for detecting red tide patches in coastal areas. We confirmed the complementarity of red tide data extracted from each sensor using an integrated red tide map. Statistical assessment showed that the sensitivity of red tide detection increased when multi-sensor data were used rather than single-sensor data. These results provide useful information for the application of multi-sensor for red tide detection.

Experimental observations of a laser suppression imaging system using pupil-plane phase elements

To help diminish the undesirable effects of laser irradiation on an imaging sensor, a pupil-plane phase element was introduced to broaden the point spread function, thereby reducing the focused laser irradiance. A sharpened image was subsequently restored via Wiener deconvolution. Successful experimental demonstrations employing a spatial light modulator in the pupil plane are reported for the vortex, axicon, and cubic phase. Furthermore, to circumvent information loss owing to zero values in the modulation transfer function, we demonstrate how images with different phase elements, combined with a joint deconvolution operation, provide an improved image.

Super-Resolution Reconstruction of High-Resolution Satellite ZY-3 TLC Images

Super-resolution (SR) image reconstruction is a technique used to recover a high-resolution image using the cumulative information provided by several low-resolution images. With the help of SR techniques, satellite remotely sensed images can be combined to achieve a higher-resolution image, which is especially useful for a two- or three-line camera satellite, e.g., the ZY-3 high-resolution Three Line Camera (TLC) satellite. In this paper, we introduce the application of the SR reconstruction method, including motion estimation and the robust super-resolution technique, to ZY-3 TLC images. The results show that SR reconstruction can significantly improve both the resolution and image quality of ZY-3 TLC images.

Validation of modeled sparse aperture post-processing artifacts

Sparse aperture imaging introduces a number of interesting image quality issues. Just as with traditional systems, resolution, signal-to-noise ratio, and post processing are all relevant to image quality. This work will examine post-processing artifacts that arise in sparse aperture imagery, which are more complex than the edge-overshoot artifacts that appear in traditional imagery. Modeling has predicted the existence of these artifacts. This work will verify that prediction with real data. Artifacts rising from various causes will be examined. It will be established that model predictions can be used in future trade studies regarding artifacting.

Genetic apertures: an improved sparse aperture design framework

The majority of optical sparse aperture imaging research in the remote sensing field has been confined to a small set of aperture layouts. While these layouts possess some desirable properties for imaging, they may not be ideal for all applications. This work introduces an optimization framework for sparse aperture layouts based on genetic algorithms as well as a small set of fitness functions for incoherent sparse aperture image quality. The optimization results demonstrate the merits of existing designs and the opportunity for creating new sparse aperture layouts.

Reducing the risk of laser damage in a focal plane array using linear pupil-plane phase elements

A compact imaging system with reduced risk of damage owing to intense laser radiation is presented. We find that a pupil phase element may reduce the peak image plane irradiance from an undesirable laser source by two orders of magnitude, thereby protecting the detector from damage. The desired scene is reconstructed in postprocessing. The general image quality equation (GIQE) [Appl. Opt.36, 8322 (1997)] is used to estimate the interpretability of the resulting images. A localized loss of information caused by laser light is also described. This system may be advantageous over other radiation protection approaches because accurate pointing and nonlinear materials are not required.

Multi-agent Orbit Design for Visual Perception Enhancement Purpose

This paper develops a robust optimization-based method to design orbits on which the sensory perception of the desired physical quantities are maximized. It also demonstrates how to incorporate various constraints imposed by many spacecraft missions, such as collision avoidance, co-orbital configuration, altitude and frozen orbit constraints along with Sun-synchronous orbit constraints. The paper specifically investigates designing orbits for constrained visual sensor planning applications as its case study. For this purpose, the key elements to form an image in such vision systems are considered and effective factors are taken into account to define a metric for perception quality. The method employs a max-min model to ensure robustness against possible perturbations and model uncertainties. While fulfilling the mission requirements, the algorithm devises orbits on which a higher level collective observation quality for the desired sides of the targets is available. The simulation results confirm the effectiveness of the proposed method for several scenarios involving low and medium Earth orbits as well as a challenging space-based space surveillance program application.

In Vivo Brain Magnetic Resonance Spectroscopy: A Measurement of Biomarker Sensitivity to Post-Processing Algorithms

Clinical translation of reported biomarkers requires reliable and consistent algorithms to derive biomarkers. However, the literature reports statistically significant differences between 1-D MRS measurements from control groups and subjects with disease states but frequently provides little information on the algorithms and parameters used to process the data. The sensitivity of in vivo brain magnetic resonance spectroscopy biomarkers is investigated with respect to parameter values for two key stages of post-acquisitional processing. Our effort is specifically motivated by the lack of consensus on approaches and parameter values for the two critical operations, water resonance removal, and baseline correction. The different stages of data processing also introduce varying levels of uncertainty and arbitrary selection of parameter values can significantly underutilize the intrinsic differences between two classes of signals. The sensitivity of biomarkers points to the need for a better understanding of how all stages of post-acquisitional processing affect biomarker discovery and ultimately, clinical translation. Our results also highlight the possibility of optimizing biomarker discovery by the careful selection of parameters that best reveal class differences. Using previously reported data and biomarkers, our results demonstrate that small changes in parameter values affect the statistical significance and corresponding effect size of biomarkers. Consequently, it is possible to increase the strength of biomarkers by selecting optimal parameter values in different spectral intervals. Our analyses with a previously reported data set demonstrate an increase in effect sizes for wavelet-based biomarkers of up to 36%, with increases in classification performance of up to 12%.

Novel compact dual-band LOROP camera with telecentricity

We report a new dual band compact oblique photography camera (LC11) that is the first to benefit from the incorporation of telecentricity. LC11 has a common front end F/6.6 telescope with 280 mm in aperture that forms its electro-optical (EO, F/7.5) and MWIR (F/5.6) modules. The design allows a substantial reduction in volume and weight due to i) the EO/MWIR compensator and relay lens groups arranged very close to the primary mirror (M1), and ii) light-weighted M1 and SiC main frame (MF) structure. Telecentricity of up to 2 and 0.2 degrees for the EO and MWIR modules, respectively, is achieved by balancing optical power among all lenses. The initial field test shows 0.32 ± 0.05 (EO)/0.20 ± 0.06 (MWIR) in measured MTF at 28 (EO) and 13 (MWIR) cycles/mm in target frequency, and an improved operability with a greater reduction in operational volume and mass than other existing LOROP cameras.

Diffuse prior monotonic likelihood ratio test for evaluation of fused image quality measures

This paper introduces a novel method to score how well proposed fused image quality measures (FIQMs) indicate the effectiveness of humans to detect targets in fused imagery. The human detection performance is measured via human perception experiments. A good FIQM should relate to perception results in a monotonic fashion. The method computes a new diffuse prior monotonic likelihood ratio (DPMLR) to facilitate the comparison of the H(1) hypothesis that the intrinsic human detection performance is related to the FIQM via a monotonic function against the null hypothesis that the detection and image quality relationship is random. The paper discusses many interesting properties of the DPMLR and demonstrates the effectiveness of the DPMLR test via Monte Carlo simulations. Finally, the DPMLR is used to score FIQMs with test cases considering over 35 scenes and various image fusion algorithms.

Application of the general image-quality equation to aberrated imagery

The regression analysis for the general image-quality equation (GIQE) [Appl. Opt. 36, 8322-8328 (1997)] was performed using image data from well-corrected optical systems. We conducted human-subject experiments to examine the use of the GIQE with aberrated imagery. A modified image-quality equation for aberrated imagery is presented based on analysis of the experimental results.

Wiener reconstruction of undersampled imagery

We derive a Fourier-domain Wiener filter for the reconstruction of undersampled imagery. The filter differs from previous implementations in that it permits adjustment of the trade-offs between sharpness of the reconstruction, noise amplification, and aliasing artifact suppression. Additionally, a net transfer function that characterizes the combined effects of the imaging system and the reconstruction process is derived. This net transfer function is valid for both unaliased and aliased spatial frequencies. The expression for the net transfer function is applicable to more general linear image sharpening algorithms.

Mission-driven evaluation of imaging system quality

Image-quality criteria are usually intended to achieve the best possible image at a given sampling rate, which is ill-suited to applications where the detection of well-defined geometric and radiometric properties of scenes or objects are paramount. The quality criterion developed here for designing observation systems is based on properties of the objects to be viewed. It is thus an object-oriented imaging quality criterion rather than an image-oriented one. We also propose to go beyond optimization and calibrate a numerical scale that can be used to rate the quality of the service delivered by any observation system.

General image-quality equation for infrared imagery

The regression-based general image-quality equation (GIQE) previously developed for visible imagery was extended to IR imagery. The equation predicts IR National Imagery Interpretability Rating Scale values as a function of scale, sharpness, and signal-to-noise ratio with the same form as the visible GIQE.

Sensor performance conversions for infrared target acquisition and intelligence-surveillance-reconnaissance imaging sensors

Target acquisition infrared imaging sensors are characterized by their minimum resolvable temperature parameter that is translated to the probability of identification (Pid) performance estimate for a given target. Intelligence-surveillance-reconnaissance (ISR) sensors are characterized by the general image quality equation to give a national imagery interpretability rating scale (NIIRS) performance estimate. Sensors, such as those on Predator and Global Hawk, will soon be used for both ISR and target acquisition purposes. We present a performance conversion that includes both sensor resolution and sensitivity. We also provide the first empirical results to our knowledge ever to be presented that relate NIIRS and Pid for a given set of targets.