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NASA NTRS1987

The linear filaments of the radio arc near the Galactic center

High-resolution radio continuum VLA images of a segment of the Galactic center arc centered at G0.16-0.15 at wavelengths of both 6 and 20 cm are presented. In this segment, the highest multiplicity of filaments, the largest degree of linear polarization, and a maximum rotation measure of -5500 rad/sq m between wavelengths 6.166 and 6.363 cm are found. The large 'helical segments' which surround the filamentary system are also shown and discussed. Based on a number of intriguing characteristics of the filamentary system, including the helical component and possible twisting of some of the filaments, a picture is considered in which the geometry of the magnetic structure is that of a cylindrically symmetric, force-free field anchored to the halo of the Galaxy. An analogy with solar flare filaments is used to discuss some aspects of the Galactic center filaments.

Yusef-Zadeh, Farhad↗

NASA NTRS1988

Sensor fusion of range and reflectance data for outdoor scene analysis

In recognizing objects in an outdoor scene, range and reflectance (or color) data provide complementary information. Results of experiments in recognizing outdoor scenes containing roads, trees, and cars are presented. The recognition program uses range and reflectance data obtained by a scanning laser range finder, as well as color data from a color TV camera. After segmentation of each image into primitive regions, models of objects are matched using various properties.

Kweon, In SO↗

NASA NTRS1994

Multimodal correlation and intraoperative matching of virtual models in neurosurgery

The multimodal correlation between different diagnostic exams, the intraoperative calibration of pointing tools and the correlation of the patient's virtual models with the patient himself, are some examples, taken from the biomedical field, of a unique problem: determine the relationship linking representation of the same object in different reference frames. Several methods have been developed in order to determine this relationship, among them, the surface matching method is one that gives the patient minimum discomfort and the errors occurring are compatible with the required precision. The surface matching method has been successfully applied to the multimodal correlation of diagnostic exams such as CT, MR, PET and SPECT. Algorithms for automatic segmentation of diagnostic images have been developed to extract the reference surfaces from the diagnostic exams, whereas the surface of the patient's skull has been monitored, in our approach, by means of a laser sensor mounted on the end effector of an industrial robot. An integrated system for virtual planning and real time execution of surgical procedures has been realized.

Ceresole, Enrico↗

NASA NTRS2001

Spatial/Spectral Identification of Endmembers from AVIRIS Data using Mathematical Morphology

During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or improved for remote sensing applications. Imaging spectrometry allows the detection of materials, objects and regions in a particular scene with a high degree of accuracy. Hyperspectral data typically consist of hundreds of thousands of spectra, so the analysis of this information is a key issue. Mathematical morphology theory is a widely used nonlinear technique for image analysis and pattern recognition. Although it is especially well suited to segment binary or grayscale images with irregular and complex shapes, its application in the classification/segmentation of multispectral or hyperspectral images has been quite rare. In this paper, we discuss a new completely automated methodology to find endmembers in the hyperspectral data cube using mathematical morphology. The extension of classic morphology to the hyperspectral domain allows us to integrate spectral and spatial information in the analysis process. In Section 3, some basic concepts about mathematical morphology and the technical details of our algorithm are provided. In Section 4, the accuracy of the proposed method is tested by its application to real hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imaging spectrometer. Some details about these data and reference results, obtained by well-known endmember extraction techniques, are provided in Section 2. Finally, in Section 5 we expose the main conclusions at which we have arrived.

Plaza, Antonio↗

NASA NTRS2013

Preliminary Measurement of Lunar Particle Shapes.

Particle shape is a basic parameter and essential for many engineering applications. Very little data is published on the shape of lunar particles. An unpublished review found that even where the same samples were studied the results were contradictory, probably because of extremely small sample sizes. Other workers have made fundamental errors in algorithms. There are many ways to measure particle shape. One common approach is to examine the particles as intersected by a plain, such as a thin section. If discrete particles can be segmented from the image, programs such as ImageJ can readily obtain shape measurements for each particle.

Rickman, Doug↗

NASA NTRS2013

Tracking the Momentum Flux of a CME and Quantifying Its Influence on Geomagnetically Induced Currents at Earth

We investigate a coronal mass ejection (CME) propagating toward Earth on 29 March 2011. This event is specifically chosen for its predominately northward directed magnetic field, so that the influence from the momentum flux onto Earth can be isolated. We focus our study on understanding how a small Earth-directed segment propagates. Mass images are created from the white-light cameras onboard STEREO which are also converted into mass height-time maps (mass J-maps). The mass tracks on these J-maps correspond to the sheath region between the CME and its associated shockfront as detected by in situ measurements at L1. A time series of mass measurements from the STEREOCOR-2A instrument is made along the Earth propagation direction. Qualitatively, this mass time series shows a remarkable resemblance to the L1 in situ density series. The in situ measurements are used as inputs into a three-dimensional (3-D) magnetospheric space weather simulation from the Community Coordinated Modeling Center. These simulations display a sudden compression of the magnetosphere from the large momentum flux at the leading edge of the CME, and predictions are made for the time derivative of the magnetic field (dBdt) on the ground. The predicted dBdt values were then compared with the observations from specific equatorially located ground stations and showed notable similarity. This study of the momentum of a CME from the Sun down to its influence on magnetic ground stations on Earth is presented as a preliminary proof of concept, such that future attempts may try to use remote sensing to create density and velocity time series as inputs to magnetospheric simulations.

time series analysis↗

NASA NTRS2018

Advanced Analytics and Big Earth Data

NASA's Earth Science Data Systems process, archive and distribute petabytes of Earth Observation data to a variety of end users. These end users will face dramatically increased data size in the near future, bringing about new challenges and opportunities in analyzing those data. One area of particular ferment currently is Machine Learning. Many Machine Learning methods are black boxes, limiting direct insight into the data's properties. However, they can be used for a variety of data enhancement purposes, such as parameter retrieval, data fusion and image classification and segmentation. The Earth Observing System Data and Information System is also evolving to host large data volumes in the cloud, enabling data proximal analysis. As part of this effort, an Analytics framework is being developed to support and enhance user analysis of the data. By using standards based services in the framework, diverse user communities can be served, while also allowing inter-system collaboration in the analysis process.

Cloud Computing↗

NASA NTRS1988

Knowledge-based low-level image analysis for computer vision systems

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

Dhawan, Atam P.↗

NASA NTRS2004

Impact of Multiscale Retinex Computation on Performance of Segmentation Algorithms

Classical segmentation algorithms subdivide an image into its constituent components based upon some metric that defines commonality between pixels. Often, these metrics incorporate some measure of "activity" in the scene, e.g. the amount of detail that is in a region. The Multiscale Retinex with Color Restoration (MSRCR) is a general purpose, non-linear image enhancement algorithm that significantly affects the brightness, contrast and sharpness within an image. In this paper, we will analyze the impact the MSRCR has on segmentation results and performance.

Rahman, Zia-ur↗

NASA NTRS2002

Onboard Autonomous Rock Shape Analysis for Mars Rovers

In the quest to maximize the scientific return of future robotic missions, it is imperative that our rovers be capable of determining the importance of the science they collect so that they may prioritize the acquisition and relay of that data. As an important step in this process, we present an automated technique to allow a rover to classify the shape and other geologic characteristics of rocks from two-dimensional photographic images and three-dimensional stereographically produced data. Experiments were conducted in the Matlab environment using images returned by JPL's Mars Pathfinder mission. Our method begins by first segmenting the rocks from the background using a combination of image intensity and height data. Various metrics are then used to classify the region's sphericity, roundness, and other geometric properties. Preliminary experiments to determine the most useful metrics were conducted by characterizing the two-dimensional rock shape while the three-dimensional shape was later studied with metrics derived from these two-dimensional techniques. Seven measures were developed and implemented. The performance of each measure was characterized by analyzing imagesfrom the Pathfinder mission and ranking the rocks according to the measured properties. Combined, the measures would provide a tool by which an automated rover could discover a greater amount of information about the data it collects, leading to a more productive mission.

image segmentation↗

NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗

NASA NTRS2002

Experimental Validation of a Neuro-Fuzzy Approach to Phasing the SIBOA Segmented Mirror Testbed

NASA is preparing to launch the Next Generation Space Telescope (NGST). This telescope will be larger than the Hubble Space Telescope, be launched on an Atlas missile rather than the Space Shuttle, have a segmented primary mirror, and be placed in a higher orbit. All these differences pose significant challenges. This effort addresses the challenge of aligning the segments of the primary mirror during the initial deployment. The segments need to piston values aligned to within one tenth of a wavelength. The present study considers using a neuro-fuzzy model of the Fraunhofer diffraction theory. The intention of the current study was to experimentally verify the algorithm derived earlier. The experimental study was to be performed on the SIBOA (Systematic Image Based Optical Alignment) test bed. Unfortunately the hardware/software for SIBOA was not ready by the end of the study period. We did succeed in capturing several images of two stacked segments with various relative phases. These images can be used to calibrate the algorithm for future implementation. This effort is a continuation of prior work. The basic effort involves developing a closed loop control algorithm to phase a segmented mirror test bed (SIBOA). The control algorithm is based on a neuro-fuzzy model of SIBOA and incorporates nonlinear observers built from observer banks. This effort involves implementing the algorithm on the SIBOA test bed.

Philip D. Olivier↗

NASA NTRS2006

Modified Recursive Hierarchical Segmentation of Data

An algorithm and a computer program that implements the algorithm that performs recursive hierarchical segmentation (RHSEG) of data have been developed. While the current implementation is for two-dimensional data having spatial characteristics (e.g., image, spectral, or spectral-image data), the generalized algorithm also applies to three-dimensional or higher dimensional data and also to data with no spatial characteristics. The algorithm and software are modified versions of a prior RHSEG algorithm and software, the outputs of which often contain processing-window artifacts including, for example, spurious segmentation-image regions along the boundaries of processing-window edges.

Tilton, James C.↗

NASA NTRS1975

Dynamic radionuclide determination of regional left ventricular wall motion using a new digital imaging device

In 47 men with arteriographically defined coronary artery disease comparative studies of left ventricular ejection fraction and segmental wall motion were made with radionuclide data obtained from the image intensifier camera computer system and with contrast cineventriculography. The radionuclide data was digitized and the images corresponding to left ventricular end-diastole and end-systole were identified from the left ventricular time-activity curve. The left ventricular end-diastolic and end-systolic images were subtracted to form a silhouette difference image which described wall motion of the anterior and inferior left ventricular segments. The image intensifier camera allows manipulation of dynamically acquired radionuclide data because of the high count rate and consequently improved resolution of the left ventricular image.

Steele, P.↗

NASA NTRS2022

Mars Terrain Segmentation with Less Labels

Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection. The latest Martian terrain segmentation methods rely on supervised learning which is very data hungry and difficult to train where only a small number of labeled samples are available. Moreover, the semantic classes are defined differently for different applications (e.g., rover traversal vs. geological) and as a result the network has to be trained from scratch each time, which is an inefficient use of resources. This research proposes a semi-supervised learning framework for Mars terrain segmentation where a deep segmentation network trained in an unsupervised manner on unlabeled images is transferred to the task of terrain segmentation trained on few labeled images. The network incorporates a backbone module which is trained using a contrastive loss function and an output atrous convolution module which is trained using a pixel-wise cross-entropy loss function. Evaluation results using the metric of segmentation accuracy show that the proposed method with contrastive pre-training outperforms plain supervised learning by 2%-10%. Moreover, the proposed model is able to achieve a segmentation accuracy of 91.1% using only 161 training images (1% of the original dataset) compared to 81.9% with plain supervised learning.

Wilson, Brian D↗

NASA NTRS2004

Formation Flying: The Future of Remote Sensing from Space

Over the next two decades a revolution is likely to occur in how remote sensing of Earth, other planets or bodies, and a range of phenomena in the universe is performed from space. In particular, current launch vehicle fairing volume and mass constraints will continue to restrict the size of monolithic telescope apertures which can be launched to little or no greater size than that of the Hubble Space Telescope, the largest aperture currently flying in space. Systems under formulation today, such as the James Webb Space Telescope will be able to increase aperture size and, hence, imaging resolution, by deploying segmented optics. However, this approach is limited as well, by our ability to control such segments to optical tolerances over long distances with highly uncertain structural dynamics connecting them. Consequently, for orders of magnitude improved resolution as required for imaging black holes, imaging planets, or performing asteroseismology, the only viable approach will be to fly a collection of spacecraft in formation to synthesize a virtual segmented telescope or interferometer with very large baselines. This paper provides some basic definitions in the area of formation flying, describes some of the strategic science missions planned in the National Aeronautics and Space Administration, and identifies some of the critical technologies needed to enable some of the most challenging space missions ever conceived which have realistic hopes of flying.

Leitner, Jesse↗

NASA NTRS2004

Formation Flying: The Future of Remote Sensing from Space

Over the next two decades a revolution is likely to occur in how remote sensing of Earth, other planets or bodies, and a range of phenomena in the universe is performed from space. In particular, current launch vehicle fairing volume and mass constraints will continue to restrict the size of monolithic telescope apertures which can be launched to accommodate only slightly more performance capability than is achievable today, such as by the Hubble Space Telescope. Systems under formulation today, such as the James Webb Space Telescope will be able to increase aperture size and, hence, imaging resolution, by deploying segmented optics. However, this approach is limited as well, by ow ability to control such segments to optical tolerances over long distances with highly uncertain structural dynamics connecting them. Consequently, for orders of magnitude improved resolution as required for imaging black holes, imaging planets, or performing asteroseismology, the only viable approach will be to fly a collection of spacecraft in formation to synthesize a virtual segmented telescope or interferometer with very large baselines. This presentation describes some of the strategic science missions planned in the National Aeronautics and Space Administration, and identifies some of the critical technologies needed to enable some of the most challenging space missions ever conceived which have realistic hopes of flying.

Leitner, Jesse↗

NASA NTRS2007

Spacecraft Formation Flying: An Overview of Missions and Technologies

Over the next two decades a revolution is likely to occur in how remote sensing of Earth, other planets or bodies, and a range of phenomena in the universe is performed from space. In particular, current launch vehicle fairing volume and mass constraints will continue to restrict the size of monolithic telescope apertures which can be launched to accommodate only slightly more performance capability than is achievable today, such as by the Hubble Space Telescope. Systems under formulation today, such as the James Webb Space Telescope, will be able to increase aperture size and, hence, imaging resolution, by deploying segmented optics. However, this approach is limited as well, by our ability to control such segments to optical tolerances over long distances with highly uncertain structural dynamics connecting them. Consequently, for orders of magnitude improved resolution as required for imaging black holes, imaging planets, or performing asteroseismology, the only viable approach will be to fly a collection of spacecraft in formation to synthesize a virtual segmented telescope or interferometer with very large baselines. This presentation highlights some of the strategic science missions planned in the National Aeronautics and Space Administration, and identifies some of the critical technologies needed to enable some of the most challenging space missions ever conceived which have realistic hopes of flying.

Leitner, Jesse↗