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NASA NTRS1989

Lithospheric buckling and intra-arc stresses: A mechanism for arc segmentation

Comparison of segment development of a number of arcs has shown that consistent relationships between segmentation, volcanism and variable stresses exists. Researchers successfully modeled these relationships using the conceptual model of lithospheric buckling of Yamaoka et al. (1986; 1987). Lithosphere buckling (deformation) provides the needed mechanism to explain segmentation phenomenon; offsets in volcanic fronts, distribution of calderas within segments, variable segment stresses and the chemical diversity seen between segment boundary and segment interior magmas.

Nelson, Kerri L.↗

NASA NTRS1992

Method and apparatus for phasing segmented mirror arrays

A method and an apparatus are disclosed for edge phasing an array of segments in a segmented primary telescope mirror using white light from a far field source and starting with the inner edge of each segment in the first ring of segments. The segments are individually phased for zero piston and tilt error with respect to the edge of a reference surface in the open center position of the telescope mirror. After edge phasing of all segments in the telescope mirror array is completed, full surface phasing can be achieved by using a conventional Shack-Hartmann technique followed by finding the RMS best fit for each segment of the mirror array.

Manhart, Paul K.↗

NASA NTRS1993

A summary of image segmentation techniques

Machine vision systems are often considered to be composed of two subsystems: low-level vision and high-level vision. Low level vision consists primarily of image processing operations performed on the input image to produce another image with more favorable characteristics. These operations may yield images with reduced noise or cause certain features of the image to be emphasized (such as edges). High-level vision includes object recognition and, at the highest level, scene interpretation. The bridge between these two subsystems is the segmentation system. Through segmentation, the enhanced input image is mapped into a description involving regions with common features which can be used by the higher level vision tasks. There is no theory on image segmentation. Instead, image segmentation techniques are basically ad hoc and differ mostly in the way they emphasize one or more of the desired properties of an ideal segmenter and in the way they balance and compromise one desired property against another. These techniques can be categorized in a number of different groups including local vs. global, parallel vs. sequential, contextual vs. noncontextual, interactive vs. automatic. In this paper, we categorize the schemes into three main groups: pixel-based, edge-based, and region-based. Pixel-based segmentation schemes classify pixels based solely on their gray levels. Edge-based schemes first detect local discontinuities (edges) and then use that information to separate the image into regions. Finally, region-based schemes start with a seed pixel (or group of pixels) and then grow or split the seed until the original image is composed of only homogeneous regions. Because there are a number of survey papers available, we will not discuss all segmentation schemes. Rather than a survey, we take the approach of a detailed overview. We focus only on the more common approaches in order to give the reader a flavor for the variety of techniques available yet present enough details to facilitate implementation and experimentation.

Spirkovska, Lilly↗

NASA NTRS2003

Development of Mirror Segments for the Constellation-X Observatory

As NASA's next major X-ray observatory, Constellation-X will have a photon collection area of 30,000 sq cm at 1 keV, which, after folding other instrumental responses, translates into an effective area of 15,000 sq cm. The observatory consists of four identical satellites each of which carries a spectroscopic X-ray telescope mirror assembly (SXT) that is 1.6 m in diameter and has a focal length of 10 m and a collection area of 7,500 sq cm at 1 keV and an angular resolution of 15 sec. HPD (half-power diameter) at the system level. Each mirror assembly consists of a large number of mirror segments precisely assembled together. Our development of the mirror segments is divided into two steps. The first one is to develop the basic approach and fabricate segments within the constraints of existing infrastructure to meet the angular resolution requirement, but not mirror segment size requirement. We have all but successfully completed this part of the development. We are now on the verge of going into the second step, that is to fabricate mirror segments of larger sizes to reduce the number of segments that have to be aligned and integrated. In this paper, we report on the requirements and the development status of the mirror segments. These assembly and other requirements of the SXT are reported elsewhere.

Zhang, W.↗

NASA NTRS2005

Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.

Tilton, James C.↗

NASA NTRS2010

Robotic Arm Comprising Two Bending Segments

The figure shows several aspects of an experimental robotic manipulator that includes a housing from which protrudes a tendril- or tentacle-like arm 1 cm thick and 1 m long. The arm consists of two collinear segments, each of which can be bent independently of the other, and the two segments can be bent simultaneously in different planes. The arm can be retracted to a minimum length or extended by any desired amount up to its full length. The arm can also be made to rotate about its own longitudinal axis. Some prior experimental robotic manipulators include single-segment bendable arms. Those arms are thicker and shorter than the present one. The present robotic manipulator serves as a prototype of future manipulators that, by virtue of the slenderness and multiple- bending capability of their arms, are expected to have sufficient dexterity for operation within spaces that would otherwise be inaccessible. Such manipulators could be especially well suited as means of minimally invasive inspection during construction and maintenance activities. Each of the two collinear bending arm segments is further subdivided into a series of collinear extension- and compression-type helical springs joined by threaded links. The extension springs occupy the majority of the length of the arm and engage passively in bending. The compression springs are used for actively controlled bending. Bending is effected by means of pairs of antagonistic tendons in the form of spectra gel spun polymer lines that are attached at specific threaded links and run the entire length of the arm inside the spring helix from the attachment links to motor-driven pulleys inside the housing. Two pairs of tendons, mounted in orthogonal planes that intersect along the longitudinal axis, are used to effect bending of each segment. The tendons for actuating the distal bending segment are in planes offset by an angle of 45 from those of the proximal bending segment: This configuration makes it possible to accommodate all eight tendons at the same diameter along the arm. The threaded links have central bores through which power and video wires can be strung (1) from a charge-coupled-device camera mounted on the tip of the arms (2) back along the interior of the arm into the housing and then (3) from within the housing to an external video monitor.

Mehling, Joshua S.↗

NASA NTRS2006

Phase Sensor for Aligning a Segmented Telescope Mirror

A phase sensor has been developed for use in aligning a segmented telescope mirror to within a fraction of a wavelength in piston. (As used here, piston signifies displacement of a mirror segment along the optical axis of the telescope.) Such precise alignment is necessary in order to realize the full benefit of the large aperture achievable through segmentation. This phase sensor is achromatic. It is based on two-wavelength shearing interferometry, and can be modified to utilize an extended or broad-band (e.g., white) light source. The sensor optics include a ruled diffraction grating and an imaging lens. The sensor can measure the piston shift between segments as well as aberrations of the segments. It can measure the surface error of an individual segment, making it possible to compensate for the error with optimal amount(s) of piston and/or tilt. The precise capture range of the sensor depends partly on the telescope design; the largest relative piston shifts measurable by use of this sensor are of the order of 100 m. The accuracy of the sensor also depends partly on the telescope design; in general, the accuracy is sufficient to enable alignment to within approximately half a wavelength. The interferometric image is digitized and processed by a simple algorithm in real time, and the output of the algorithm can be used to maintain alignment in real time, even in the presence of atmospheric turbulence. The sensor is robust. Through calibration, it can be made insensitive to (and, hence, tolerant of) misalignments and aberrations of its own optics, most aberrations of the telescope as a whole (in contradistinction to aberrations of individual segments), and most aberrations introduced by atmospheric turbulence

Stahl, Philip↗

NASA NTRS2011

A Parametric Finite-Element Model for Evaluating Segmented Mirrors with Discrete, Edgewise Connectivity

Since future astrophysics missions require space telescopes with apertures of at least 10 meters, there is a need for on-orbit assembly methods that decouple the size of the primary mirror from the choice of launch vehicle. One option is to connect the segments edgewise using mechanisms analogous to damped springs. To evaluate the feasibility of this approach, a parametric ANSYS model that calculates the mode shapes, natural frequencies, and disturbance response of such a mirror, as well as of the equivalent monolithic mirror, has been developed. This model constructs a mirror using rings of hexagonal segments that are either connected continuously along the edges (to form a monolith) or at discrete locations corresponding to the mechanism locations (to form a segmented mirror). As an example, this paper presents the case of a mirror whose segments are connected edgewise by mechanisms analogous to a set of four collocated single-degree-of-freedom damped springs. The results of a set of parameter studies suggest that such mechanisms can be used to create a 15-m segmented mirror that behaves similarly to a monolith, although fully predicting the segmented mirror performance would require incorporating measured mechanism properties into the model. Keywords: segmented mirror, edgewise connectivity, space telescope

Gersh-Range, Jessica A.↗

NASA NTRS2012

Precise Alignment and Permanent Mounting of Thin and Lightweight X-ray Segments

To provide observations to support current research efforts in high energy astrophysics. future X-ray telescope designs must provide matching or better angular resolution while significantly increasing the total collecting area. In such a design the permanent mounting of thin and lightweight segments is critical to the overall performance of the complete X-ray optic assembly. The thin and lightweight segments used in the assemhly of the modules are desigued to maintain and/or exceed the resolution of existing X-ray telescopes while providing a substantial increase in collecting area. Such thin and delicate X-ray segments are easily distorted and yet must be aligned to the arcsecond level and retain accurate alignment for many years. The Next Generation X-ray Optic (NGXO) group at NASA Goddard Space Flight Center has designed, assembled. and implemented new hardware and procedures mth the short term goal of aligning three pairs of X-ray segments in a technology demonstration module while maintaining 10 arcsec alignment through environmental testing as part of the eventual design and construction of a full sized module capable of housing hundreds of X-ray segments. The recent attempts at multiple segment pair alignment and permanent mounting is described along with an overview of the procedure used. A look into what the next year mll bring for the alignment and permanent segment mounting effort illustrates some of the challenges left to overcome before an attempt to populate a full sized module can begin.

Biskach, Michael P.↗

NASA NTRS2013

Advanced Dispersed Fringe Sensing Algorithm for Coarse Phasing Segmented Mirror Telescopes

Segment mirror phasing, a critical step of segment mirror alignment, requires the ability to sense and correct the relative pistons between segments from up to a few hundred microns to a fraction of wavelength in order to bring the mirror system to its full diffraction capability. When sampling the aperture of a telescope, using auto-collimating flats (ACFs) is more economical. The performance of a telescope with a segmented primary mirror strongly depends on how well those primary mirror segments can be phased. One such process to phase primary mirror segments in the axial piston direction is dispersed fringe sensing (DFS). DFS technology can be used to co-phase the ACFs. DFS is essentially a signal fitting and processing operation. It is an elegant method of coarse phasing segmented mirrors. DFS performance accuracy is dependent upon careful calibration of the system as well as other factors such as internal optical alignment, system wavefront errors, and detector quality. Novel improvements to the algorithm have led to substantial enhancements in DFS performance. The Advanced Dispersed Fringe Sensing (ADFS) Algorithm is designed to reduce the sensitivity to calibration errors by determining the optimal fringe extraction line. Applying an angular extraction line dithering procedure and combining this dithering process with an error function while minimizing the phase term of the fitted signal, defines in essence the ADFS algorithm.

Spechler, Joshua A.↗

NASA NTRS2011

Metric Learning for Hyperspectral Image Segmentation

We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

Compact Reconnaissance Imaging Spectrometer (CRISM↗

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

Experimental Verification of Dispersed Fringe Sensing as a Segment Phasing Technique using the Keck Telescope

Dispersed fringe sensing (DFS) is an efficient and robust method for coarse phasing of segmented primary mirrors (from one quarter of a wavelength to as much as the depth of focus of a single segment, typically several tens of microns). Unlike phasing techniques currently used for ground-based segmented telescopes, DFS does not require the use of edge sensors in order to sense changes in the relative heights of adjacent segments; this makes it particularly well suited for phasing of space-borne segmented telescopes, such as the James Webb Space Telescope. We validate DFS by using it to measure the piston errors of the segments of one of the Keck telescopes. The results agree with those of the Shack-Hartmann-based phasing scheme currently in use at Keck to within 2% over a range of initial piston errors of +/-16 (mu)m.

algorithm↗

NASA NTRS2012

How to Calibrate Edge Sensors on Segmented Mirror Telescopes

The next generation of large ground based telescopes use segmented mirrors. Sensors mounted on the edge of the segments measure the relative heights of the segments. The segments are actively controlled in height by three actuators per segment, but lateral motion is only passively constrained. Thus there will be some small change in the gap and shear between segments as changing telescope orientation and temperature make small distortions in the telescope structure. These "in-plane" motions place an additional performance burden on the edge sensors, and on the precision with which they must be mounted relative to the optical surface. In addition, both the scaling and offset of sensor edge height readings also vary with changes in gap.

segmented mirrors↗

NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗

NASA NTRSDate not supplied

1d-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Machine learning↗

NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗

NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗