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NASA NTRS2017

Coronagraph Design Optimization for Segmented Aperture Telescopes

The goal of directly imaging Earth-like planets in the habitable zone of other stars has motivated the design of coronagraphs for use with large segmented aperture space telescopes. In order to achieve an optimal trade-o between planet light throughput and di racted starlight suppression, we consider coronagraphs comprised of a stage of phase control implemented with deformable mirrors (or other optical elements), pupil plane apodization masks (gray scale or complex valued), and focal plane masks (either amplitude only or complex-valued, including phase only such as the vector vortex coronagraph). The optimization of these optical elements, with the goal of achieving 10 or more orders of magnitude in the suppression of on-axis (starlight) di racted light, represents a challenging non-convex optimization problem with a nonlinear dependence on control degrees of freedom. We develop a new algorithmic approach to the design optimization problem, which we call the "Auxiliary Field Optimization" (AFO) algorithm. The central idea of the algorithm is to embed the original optimization problem, for either phase or amplitude (apodization) in various planes of the coronagraph, into a problem containing additional degrees of freedom, speci cally ctitious "auxiliary" electric elds which serve as targets to inform the variation of our phase or amplitude parameters leading to good feasible designs. We present the algorithm, discuss details of its numerical implementation, and prove convergence to local minima of the objective function (here taken to be the intensity of the on-axis source in a "dark hole" region in the science focal plane). Finally, we present results showing application of the algorithm to both unobscured o -axis and obscured on-axis segmented telescope aperture designs. The application of the AFO algorithm to the coronagraph design problem has produced solutions which are capable of directly imaging planets in the habitable zone, provided end-to-end telescope system stability requirements can be met. Ongoing work includes advances of the AFO algorithm reported here to design in additional robustness to a resolved star, and other phase or amplitude aberrations to be encountered in a real segmented aperture space telescope.

Redding, Dave↗

NASA NTRS2021

Contextual Segmentation of Fire Spotting Regions Through Satellite-Augmented Autonomous Modular Sensor Image

Globally, forest fires remain a significant threat to human and environmental wellbeing. Towards mitigating the impacts of forest fires, it is critical that accurate and updated information regarding not only the fire line, but also nearby human settlements, vegetation, and water sources is reported quickly to emergency services. However, while existing UAS-based fire detection methods are effective, they largely do not report the contextual environmental information necessary to best serve nearby communities in disaster response. Additionally, modern advancements in deep learning offer new approaches for image segmentation which may improve classification accuracy beyond current pixel-wise indices. In this work, we benchmark the performance of these modern segmentation techniques in locating both fire lines and environmental features in historical Autonomous Modular Sensor imagery. Furthermore, we augment these outputs with satellite imagery segmentation towards developing a robust contextual mapping tool for rapid emergency fire response and decision making.

Nikhil Behari↗

NASA NTRSDate not supplied

Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.

quantum↗

NASA NTRS2022

Microstructure Segmentation With Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗

NASA NTRS2006

Performance of Dispersed Fringe Sensor in the Presence of Segmented Mirror Aberrations: Modeling and Simulation

Dispersed Fringe Sensing (DFS) is an efficient and robust method for coarse phasing of a segmented primary mirror such as the James Webb Space Telescope (JWST). In this paper, modeling and simulations are used to study the effect of segmented mirror aberrations on the fringe image, DFS signals and DFS detection accuracy. The study has shown due to the pixilation spatial filter effect from DFS signal extraction the effect of wavefront error is reduced and DFS algorithm will be more robust against wavefront aberration by using multi-trace DFS approach. We also studied the JWST Dispersed Hartmann Sensor (DHS) performance in presence of wavefront aberrations caused by the gravity sag and we use the scaled gravity sag to explore the JWST DHS performance relationship with the level of the wavefront aberration. This also includes the effect from line-of-sight jitter.

segmented mirrors↗

NASA NTRS2014

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

Light Detection & Ranging (LIDAR)↗

NASA NTRS2014

Incorporating Edge Information into Best Merge Region-Growing Segmentation

We have previously developed a best merge region-growing approach that integrates nonadjacent region object aggregation with the neighboring region merge process usually employed in region growing segmentation approaches. This approach has been named HSeg, because it provides a hierarchical set of image segmentation results. Up to this point, HSeg considered only global region feature information in the region growing decision process. We present here three new versions of HSeg that include local edge information into the region growing decision process at different levels of rigor. We then compare the effectiveness and processing times of these new versions HSeg with each other and with the original version of HSeg.

Image segmentation↗

NASA NTRS2010

Shack-Hartmann Phasing of Segmented Telescopes: Systematic Effects from Lenslet Arrays

The segments in the Keck telescopes are routinely phased using a Shack-Hartmann wavefront sensor with sub-apertures that span adjacent segments. However, one potential limitation to the absolute accuracy of this technique is that it relies on a lenslet array (or a single lens plus a prism array) to form the subimages. These optics have the potential to introduce wavefront errors and stray reflections at the subaperture level that will bias the phasing measurement. We present laboratory data to quantify this effect, using measured errors from Keck and two other lenslet arrays. In addition, as part of the design of the Thirty Meter Telescope Alignment and Phasing System we present a preliminary investigation of a lenslet-free approach that relies on Fresnel diffraction to form the subimages at the CCD. Such a technique has several advantages, including the elimination of lenslet aberrations.

segmented mirrors↗

NASA NTRSDate not supplied

Design and performance analysis of a PIAACMC coronagraph on a segmented aperture

The Phase-Induced Amplitude Apodization Complex Mask Coronagraph (PIAACMC) offers high throughput and small inner working angle (IWA) for direct imaging of exoplanets. PIAACMC is also compatible with segmented apertures, which is important for the success of future large telescopes, such as the Large UV / Optical/ Infrared (LUVOIR) concept currently under way to inform NASA's2020 decadal survey. We present a design of PIAACMC for one of the segmented apertures considered for LUVOIR and simulate its performance with wavefront control, in monochromatic as well as broadband light. We characterize its performance in terms of throughput, inner working angle, contrast, area of the dark zone, and sensitivity to low order aberrations, in particular tip/tilt. In addition, we study the resilience to large stellar angular sizes and polarization. Finally, we compute the "performance map", which is a recently introduced figure of merit for coronagraphs

Exoplanets↗

NASA NTRSDate not supplied

A Markov Random Field Model for Texture-based Segmentation of Small Cracks in Thin InconelTubes

Markov random fields have been used for image segmentation since their introduction in the1980s. This work applies a method from Principal Component Thermography to enhance thecontrast in damage regions in 3D images derived from X-ray computed tomography (CT)inspections. The developed method is applied to sizing of small cracks in thin Inconel tubes designed as probability of detection (POD) samples for radiographic inspection.Misclassification errors arising from artifacts due to beam-hardening are reduced by fitting the boundary of the segmented damage region with the arc of an ellipse. Results are comparedagainst those obtained through manual inspection.

Image segmentation↗

NASA NTRS1967

Segmented, arch-bound carbon seal is pressure loaded

Conventional segmented carbon seal has a low leakage rate and minimum loading requirements for a high pressure, large diameter fluid impeller shaft with large axial and radial movements. Modifications in the segments allow part of the load to be carried in hoop stress.

Burcham, R. E.↗

NASA NTRS1974

Noise measurements obtained during engineering evaluation of two-segment approaches in a 727-200 aircraft

A series of noise measurements were made during engineering evaluation tests of two-segment approaches in a 727-200 aircraft equipped with acoustically treated nacelles. A two-segment approach having a 6-degree upper glide slope angle intercepting the Instrument Landing System (ILS) 2.9-degree glide slope at an altitude of 690 feet gave a 5-EPNdB decrease in measured noise at distances greater than 3 nautical miles from the runway threshold when compared with a normal ILS approach. Several of the noise measurements were taken under adverse weather conditions which were outside the specified limits of FAR Part 36. This may introduce uncertainties into the data from several approaches.

Tanner, C. S.↗

NASA NTRS1973

Flight evaluation of two segment approaches for jet transport noise abatement

A 75 flight-hour operational evaluation was conducted with a representative four-engine fan-jet transport in a representative airport environment. The flight instrument systems were modified to automatically provide pilots with smooth and continuous pitch steering command information during two-segment approaches. Considering adverse weather, minimum ceiling and flight crew experience criteria, a transition initiation altitude of approximately 800 feet AFL would have broadest acceptance for initiating two-segment approach procedures in scheduled service. The profile defined by the system gave an upper glidepath of approximately 6 1/2 degrees. This was 1/2 degree greater than inserted into the area navigation system. The glidepath error is apparently due to an erroneous along-track, distance-to-altitude profile.

Rogers, R. A.↗

NASA NTRS1974

Applicability of NASA (ARC) two-segment approach procedures to Boeing Aircraft

An engineering study to determine the feasibility of applying the NASA (ARC) two-segment approach procedures and avionics to the Boeing fleet of commercial jet transports is presented. This feasibility study is concerned with the speed/path control and systems compability aspects of the procedures. Path performance data are provided for representative Boeing 707/727/737/747 passenger models. Thrust margin requirements for speed/path control are analyzed for still air and shearing tailwind conditions. Certification of the two-segment equipment and possible effects on existing airplane certification are discussed. Operational restrictions on use of the procedures with current autothrottles and in icing or reported tailwind conditions are recommended. Using the NASA/UAL 727 procedures as a baseline, maximum upper glide slopes for representative 707/727/737/747 models are defined as a starting point for further study and/or flight evaluation programs.

Allison, R. L.↗

NASA NTRS1973

Noise measurements taken at LAX during operational evaluation of two-segment approaches in a 727-200 aircraft

A series of seven noise measurements were made each day over a period of fifteen days. The first and last flights each day were made by a specially instrumented 727-200 aircraft being used to evaluate the operational effectiveness of two-segment noise abatement approaches in scheduled service. Noise measurements were made to determine the noise reduction benefits of the two-segment approaches.

Tanner, C. S.↗

NASA NTRS1974

The Out of Service Guest Pilot Evaluation of the Two-segment Noise Abatement Approach in the Boeing B727-200

Guest pilot evaluation results of an approach profile modification for reducing ground level noise under the approach of jet aircraft runways are reported. Evaluation results were used to develop a two segmented landing approach procedure and equipment necessary to obtain pilot, airline, and FAA acceptance of the two segmented flight as a routine way of operating aircraft on approach and landing. Data are given on pilot workload and acceptance of the procedure.

Nylen, W. E.↗

NASA NTRS1974

Engineering simulation evaluation of the two-segment noise abatement approach in the Douglas DC-8-61

The development and operational evaluation are discussed of a noise abatement two-segment approach which is safe and operationally acceptable for routine use in air carrier service. An engineering simulation evaluation was conducted; it was found that the entry speed and initial configuration are more important in the DC-8-61 than in the 727 because of the DC-8's aerodynamic cleanliness and drag programming constraints. It was also found that the altitude required for the DC-8 upper and lower transitions is less than the 727 transitions principally due to the pitching moment from the underslung DC-8 engines as compared to the 727 engines which produce no pitching moment with a change in thrust. The 727 approach was a reduced flaps (30 degrees) procedure with a 10-knot airspeed bleed in the lower transition; the DC-8 approach is a full flaps (50 degrees) procedure with constant airspeed established as soon as possible after upper segment transition.

Nylen, W. E.↗

NASA NTRS1974

Jet transport noise - A comparison of predicted and measured noise for ILS and two-segment approaches

Centerline noise measured during standard ILS and two-segment approaches in DC-8-61 aircraft were compared with noise predicted for these procedures using an existing noise prediction technique. Measured data is considered to be in good agreement with predicted data. Ninety EPNdB sideline locations were calculated from flight data obtained during two-segment approaches and were compared with predicted 90 EPNdB contours that were computed using three different models for excess ground attenuation and a contour with no correction for ground attenuation. The contour not corrected for ground attenuation was in better agreement with the measured data.

White, K. C.↗