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NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗

NASA NTRS2012

High Contrast Vacuum Nuller Testbed (VNT) Contrast, Performance and Null Control

Herein we report on our contrast assessment and the development, sensing and control of the Vacuum Nuller Testbed to realize a Visible Nulling Coronagraphy (VNC) for exoplanet detection and characterization. Tbe VNC is one of the few approaches that works with filled, segmented and sparse or diluted-aperture telescope systems. It thus spans a range of potential future NASA telescopes and could be flown as a separate instrument on such a future mission. NASA/Goddard Space Flight Center has an established effort to develop VNC technologies, and an incremental sequence of testbeds to advance this approach and its critical technologies. We discuss the development of the vacuum Visible Nulling Coronagraph testbed (VNT). The VNT is an ultra-stable vibration isolated testbed that operates under closed-loop control within a vacuum chamber. It will be used to achieve an incremental sequence of three visible-light nulling milestones with sequentially higher contrasts of 10(exp 8), 10(exp 9) and ideally 10(exp 10) at an inner working angle of 2*lambda/D. The VNT is based on a modified Mach-Zehnder nulling interferometer, with a "W" configuration to accommodate a hex-packed MEMS based deformable mirror, a coherent fiber bundle and achromatic phase shifters. We discuss the laboratory results, optical configuration, critical technologies and the null sensing and control approach.

Lyon, Richard G.↗

NASA NTRS1984

The future of satellite remote sensing: A worldwide assessment and prediction

A frame-work in which to assess and predict the future prospects for satellite remote sensing markets is provided. The scope of the analysis is the satellite-related market for data, equipment, and services. It encompasses both domestic and international markets and contains an examination of the various market characteristics by market segment (e.g., Federal Government, State and Local Governments, Academic Organizations, Industrial Companies, and Individuals) and primary applications areas (e.g., Geology, Forestry, Land Resource Management, Agriculture and Cartography). The forecasts are derived from an analysis of both U.S. and foreign market data. The evolution and current status of U.S. and Foreign markets to arrive at market growth rates is evaluated. Circumstances and events which are likely to affect the future market development are examined. A market growth scenario is presented that is consistent with past data sales trends and takes into account the dynamic nature of the future satellite remote sensing market. Several areas of current and future business opportunities available in this market are discussed. Specific worldwide forecasts are presented in three market sectors for the period 1980 to 1990.

Spann, G. W.↗

NASA NTRS2011

Integrating Machine Learning into a Crowdsourced Model for Earthquake-Induced Damage Assessment

On January 12th, 2010, a catastrophic 7.0M earthquake devastated the country of Haiti. In the aftermath of an earthquake, it is important to rapidly assess damaged areas in order to mobilize the appropriate resources. The Haiti damage assessment effort introduced a promising model that uses crowdsourcing to map damaged areas in freely available remotely-sensed data. This paper proposes the application of machine learning methods to improve this model. Specifically, we apply work on learning from multiple, imperfect experts to the assessment of volunteer reliability, and propose the use of image segmentation to automate the detection of damaged areas. We wrap both tasks in an active learning framework in order to shift volunteer effort from mapping a full catalog of images to the generation of high-quality training data. We hypothesize that the integration of machine learning into this model improves its reliability, maintains the speed of damage assessment, and allows the model to scale to higher data volumes.

crowdsourcing↗

NASA NTRS2023

JWST NIRCam Defocused Imaging Photometric Stability Performance and How It Can Sense Mirror Tilts

We use JWST NIRCam short-wavelength photometry to capture a transit lightcurve of the exoplanet HAT-P-14 b to assess performance as part of instrument commissioning. The short-wavelength precision is 152 ppm per 27 s integration as measured over the full time series compared to a theoretical limit of 107 ppm, after corrections to spatially correlated 1/f noise. Persistence effects from charge trapping are well fit by an exponential function with short characteristic timescales, settling on the order of 5–15 minutes. The short-wavelength defocused photometry is also uniquely well suited to measure the real-time wave-front error of JWST. Analysis of the images and reconstructed wave-front maps indicates that two different hexagonal primary mirror segments exhibited “tilt events,” where they changed orientation rapidly in less than ∼1.4 s. In some cases, the magnitude and timing of the flux jumps caused by tilt events can be accurately predicted with a telescope model. These tilt events can be sensed by simultaneous longer-wavelength NIRCam grism spectral images alone in the form of changes to the point-spread function, diagnosed from the full width at half maximum. They can also be sensed with the fine guidance sensor instrument from difference images. Tilt events possibly from sudden releases of stress in the backplane structure behind the mirrors were expected during the commissioning period because they were found in ground-based testing. Tilt events have shown signs of decreasing in frequency but have not disappeared completely. The detectors exhibit some minor (less than 1%) deviations from linear behavior in the first few groups of each integration, potentially impacting absolute fluxes and transit depths on bright targets, where only a handful of groups are possible. Overall, the noise is within 50% of the theoretical photon noise and read noise. This bodes well for high-precision measurements of transiting exoplanets and other time variable targets.

Everett Schlawin↗

NASA NTRS2011

Vacuum Nuller Testbed (VNT) Performance, Characterization and Null Control: Progress Report

Herein we report on the development. sensing and control and our first results with the Vacuum Nuller Testbed to realize a Visible Nulling Coronagraph (VNC) for exoplanet coronagraphy. The VNC is one of the few approaches that works with filled. segmented and sparse or diluted-aperture telescope systems. It thus spans a range of potential future NASA telescopes and could be Hown as a separate instrument on such a future mission. NASA/Goddard Space Flight Center (GSFC) has a well-established effort to develop VNC technologies. and has developed an incremental sequence of VNC testbeds to advance this approach and the enabling technologies associated with it. We discuss the continued development of the vacuum Visible Nulling Coronagraph testbed (VNT). Tbe VNT is an ultra-stable vibration isolated testbed that operates under closed-loop control within a vacuum chamber. It will be used to achieve an incremental sequence of three visible-light nulling milestones with sequentially higher contrasts of 10(sup 8), 10(sup 9) and ideally 10(sup 10) at an inner working angle of 2*lambda/D. The VNT is based on a modified Mach-Zehnder nulling interferometer, with a "W" configuration to accommodate a hex-packed MEMS based deformable mirror, a coherent fiber bundle and achromatic phase shifters. We discuss the initial laboratory results, the optical configuration, critical technologies and the null sensing and control approach.

Lyon, Richard G.↗

NASA NTRS1981

LANDSAT-D data format control book. Volume 5: (Payload)

The LANDSAT-D flight segment payload is the thematic mapper and the multispectral scanner. Narrative and visual descriptions of the LANDSAT-D payload data handling hardware and data flow paths from the sensing instruments through to the GSFC LANDSAT-D data management system are provided. Key subsystems are examined.

Andrew, H.↗

NASA NTRS1994

Active microwaves

Research on the use of active microwaves in remote sensing, presented during plenary and poster sessions, is summarized. The main highlights are: calibration techniques are well understood; innovative modeling approaches have been developed which increase active microwave applications (segmentation prior to model inversion, use of ERS-1 scatterometer, simulations); polarization angle and frequency diversity improves characterization of ice sheets, vegetation, and determination of soil moisture (X band sensor study); SAR (Synthetic Aperture Radar) interferometry potential is emerging; use of multiple sensors/extended spectral signatures is important (increase emphasis).

Evans, D.↗

NASA NTRS2010

Wavefront Sensing of X-Ray Telescopes

Phase Retrieval analysis of off-axis or defocused focal-plane data from telescope optics has been proven effective in understanding misalignments and optical aberrations in normal incidence telescopes. The approach is used, e.g., in commissioning of the James Webb Space Telescope (JWST) segmented primary mirror. There is a similar need for evaluating low-order figure errors of grazing incidence mirrors and nested telescope assemblies. When implemented in these systems, phase retrieval does not depend on normal incidence access to each mirror (shell) surface and, therefore, provides an effective means for evaluating nested x-ray telescopes during integration and test. We have applied a well-known phase retrieval algorithm to grazing incidence telescopes. The algorithm uses the Levenberg-Marquardt optimization procedure to perform a non-linear least-squares fit of the telescope Point Spread Function (PSF). The algorithm can also retrieve low order figure errors at visible wavelengths where optical diffraction is the dominant defect in the PSF. In this paper we will present the analytical approach and its implementation for grazing incidence mirrors of the International X-Ray Observatory (IXO). We analyze the effects of low order axial surface errors individually, and in combination on the system PSF at 633 nanometers. We demonstrate via modeling that the wavefront sensing algorithm can recover axial errors (of the grazing incidence mirrors) to a small fraction of the known axial figure errors using simulated PSFs as input data to the algorithm.

Saha, Timo↗

NASA NTRS1987

High-resolution CCD imagers using area-array CCD's for sensing spectral components of an optical line image

CCD imagers with a novel replicated-line-imager architecture are abutted to form an extended line sensor. The sensor is preceded by optics having a slit aperture and having an optical beam splitter or astigmatic lens for projecting multiple line images through an optical color-discriminating stripe filter to the CCD imagers. A very high resolution camera suitable for use in a satellite, for example, is thus provided. The replicated-line architecture of the imager comprises an area-array CCD, successive rows of which are illuminated by replications of the same line segment, as transmitted by respective color filter stripes. The charge packets formed by accumulation of photoresponsive charge in the area-array CCD are read out row by row. Each successive row of charge packets is then converted from parallel to serial format in a CCD line register and its amplitude sensed to generate a line of output signal.

Elabd, Hammam↗

NASA NTRS1984

Surface accuracy measurement sensor test on a 50-meter antenna surface model

The Surface Accuracy Measurement Sensor (SAMS) is a telescope with a focal plane photo electric detector that senses the lateral position of light source targets in its field of view. After extensive laboratory testing the engineering breadboard sensor system was installed and tested on a 30 degree segment of a 50-meter diameter, mesh surface, antenna model. Test results correlated well with the laboratory tests and indicated accuracies of approximately 0.59 arc seconds at 21 meters range. Test results are presented and recommendations given for sensor improvements.

Spiers, R. B.↗

NASA NTRS1984

Texture classification using autoregressive filtering

A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.

Lawton, W. M.↗

NASA NTRS1994

System and process for detecting and monitoring surface defects

A system and process for detecting and monitoring defects in large surfaces such as the field joints of the container segments of a space shuttle booster motor. Beams of semi-collimated light from three non-parallel fiber optic light panels are directed at a region of the surface at non-normal angles of expected incidence. A video camera gathers some portion of the light that is reflected at an angle other than the angle of expected reflectance, and generates signals which are analyzed to discern defects in the surface. The analysis may be performed by visual inspection of an image on a video monitor, or by inspection of filtered or otherwise processed images. In one alternative embodiment, successive predetermined regions of the surface are aligned with the light source before illumination, thereby permitting efficient detection of defects in a large surface. Such alignment is performed by using a line scan gauge to sense the light which passes through an aperture in the surface. In another embodiment a digital map of the surface is created, thereby permitting the maintenance of records detailing changes in the location or size of defects as the container segment is refurbished and re-used. The defect detection apparatus may also be advantageously mounted on a fixture which engages the edge of a container segment.

Mueller, Mark K.↗

NASA NTRS2015

Flexible Electrostatic Technologies for Capture and Handling, Phase 1

Fundamental to many of NASA's in-space transportation missions is the capture and handling of various objects and vehicles in various orbits for servicing, debris disposal, sample retrieval, and assembly without the benefit of sufficient grapple fixtures and docking ports. To perform similar material handling tasks on Earth, pincher grippers, suction grippers, or magnetic chucks are used, but are unable to reliably grip aluminum and composite spacecraft, insulation, radiators, solar arrays, or extra-terrestrial objects in the vacuum of outer space without dedicated handles in the right places. The electronic Flexible Electrostatic Technologies for space Capture and Handling (FETCH) will enable reliable and compliant gripping (soft dock) of practically any object in various orbits or surfaces without dedicated mechanical features, very low impact capture, and built-in proximity sensing without any conventional actuators. Originally developed to handle semiconductor and glass wafers during vacuum chamber processing without contamination, the normal rigid wafer handling chucks are replaced with thin metal foil segments laminated in flexible insulation driven by commercial off-the-shelf solid state, high-voltage power supplies. Preliminary testing in NASA Marshall Space Flight Center's (MSFC's) Flat Floor Robotics Lab demonstrated compliant alignment and gripping with a full-sized, 150-lb microsat mockup and translation before a clean release with a flip of a switch. The flexible electrostatic gripper pads can be adapted to various space applications with different sizes, shapes, and foil electrode layouts even with openings through the gripper pads for addition of guidance sensors or injection of permanent adhesives. With gripping forces estimated between 0.5 and 2.5 lb/in2 or 70-300 lb/ft2 of surface contact, the FETCH can turn on and off rapidly and repeatedly to enable sample handling, soft docking, in-space assembly, precision relocation, and surface translation for accurate anchoring.

Bryan, Thomas↗

NASA NTRS2019

Picometer Level Spatial Metrology for Next Generation Telescopes

During the testing of the primary mirror segments for JWST, our team realized that some of the tools and techniques we had developed could be pushed further to achieve picometer resolution.We began developing incremental techniques for measuring, controlling, sensing to picometer levelsSeveral recent peer reviewed papers have shown that we can measure this level of change, control it with actuators, and potentially even develop active architectures using these ideasWe will show how the work on JWST evolved to systems applicable to measure picometer and even sub-picometer levels, show the results, and discuss implications for future telescope like LUVOIR and Habex

Keski-Kuha, R.↗

NASA NTRSDate not supplied

Satellite-Derived Imagery and Transfer Learning: A Novel Technique for Land Cover Classification

Land cover classification is a continuing research topic due to its relevance to land use and land cover changes from impacts such as climate change, agriculture, urbanization, and hazardous weather. Simple access to frequently changing land cover classifications could provide knowledge and decision support to various researchers and agencies across the globe for each of above-mentioned and related influences. This research aims to provide a novel technique for land cover classification of remote sensing imagery; harnessing Artificial Neural Networks and transfer learning (TL). Knowledge sharing techniques within machine learning are typically utilized when training datasets are sparse or transitions between data modalities is required. In this case, two data modalities, multi and hyperspectral data, are considered for knowledge transfer. The large number of continuous spectral bands available from hyperspectral sensors typically provide increased sophisticated land classification capability compared to more traditional multispectral imagery with limited discrete spectral bands. However, large-scale, frequent access to hyperspectral imagery is relatively limited. The proposed classification technique would therefore prove useful in regions in which hyperspectral data are not readily available for classification but multispectral data are. Image segmentation models are trained on each multi and hyperspectral datasets. Knowledge sharing techniques are then applied to each model to understand what knowledge, if any, is gained when moving between the data modalities. The datasets utilized for training and testing a U-Net model include the European Space Agency’s multispectral imager Sentinel-2 and the hyperspectral German Aerospace Center’s Earth Sensing Imaging Spectrometer (DESIS). Initially, only two classes, land and water, are classified for simplicity. However, more complex classes can be added if knowledge sharing is successful. The workflow for image classification through supervised image segmentation with a U-Net model will be discussed along with metrics calculated before and after TL for both Sentinel-2 and DESIS data are applied. Additionally, future steps to advance the sophistication of this technique as well as other applicable methodologies will be explored.

Emily Foshee↗

NASA NTRS2018

The Large UV/Optical/Infrared Surveyor (LUVOIR): Decadal Mission Study Update

NASA commissioned the study of four large mission concepts, including the Large Ultraviolet / Optical / Infrared (LUVOIR) Surveyor, to be evaluated by the 2020 Decadal Survey in Astrophysics. In response, the Science and Technology Definition Team (STDT) identified a broad range of science objectives for LUVOIR that include the direct imaging and spectral characterization of habitable exoplanets around sun-like stars, the study of galaxy formation and evolution, the exchange of matter between galaxies, star and planet formation, and the remote sensing of Solar System objects. To meet these objectives, the LUVOIR Study Office, located at NASA's Goddard Space Flight Center (GSFC), completed the first design iteration of a 15-m segmented-aperture observatory that would be launched by the Space Launch System (SLS) Block 2 configuration. The observatory includes four serviceable instruments: the Extreme Coronagraph for Living Planetary Systems (ECLIPS), an optical / near-infrared coronagraph capable of delivering 10(exp -10) contrast at inner working angles as small as 2 lambda/D; the LUVOIR UV Multi-object Spectrograph (LUMOS), which will provide low- and medium-resolution UV (100 - 400 nm) multi-object imaging spectroscopy in addition to far-UV imaging; the High Definition Imager (HDI), a high-resolution wide-field-of-view NUV-Optical-NIR imager; and Pollux, a high-resolution UV spectro-polarimeter being contributed by Centre National d'Etudes Spatiales (CNES). The study team has executed a second design iteration to further improve upon the 15-m concept, while simultaneously studying an 8-m concept. In these proceedings, we provide an update on these two architectures.

cosmic origins↗