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NASA NTRS1993

Instantaneous velocity field imaging instrument for supersonic reacting flows

The technical tasks conducted to develop and demonstrate a new gas velocity measurement technique for high enthalpy reacting flows is described. The technique is based on Doppler-shifted Planar Laser-induced Fluorescence (PLIF) imaging of the OH radical. The imaging approach permits, in principle, single-shot measurements of the 2-D distribution of a single velocity component in the measurement plane, and is thus a technique of choice for applications in high enthalpy transient flow facilities. In contrast to previous work in this area, the present program demonstrated an approach which modified the diagnostic technique to function under the constraints of practical flow conditions of engineering interest, rather than vice-versa. In order to accomplish the experimental demonstrations, the state-of-the-art in PLIF diagnostic techniques was advanced in several ways. Each of these tasks is described in detail and is intended to serve as a reference in supporting the transition of this new capability to the fielded PLIF instruments now installed at several national test facilities. Among the new results of general interest in LlF-based flow diagnostics, a detailed set of the first measurements of the collisional broadening and shifting behavior of OH (1,0) band transitions in H7-air combustion environments is included. Such measurements are critical in the design of a successful strategy for PLIF velocity imaging; they also relate to accurate concentration and temperature measurements, particularly in compressible flow regimes. Furthermore, the results shed new light on the fundamental relationship between broadening and energy transfer collisions in OH A(sup 2)Sigma(+)v(sup ') = 1. The first single-pulse, spectrally-resolved measurements of the output of common pulsed dye lasers were also produced during the course of this effort. As with the OH broadening measurements, these data are a significant aspect of a successful velocity imaging strategy, and also have potential implications for many other LIF measurement techniques. Our results indicated the need to modify the commercially available laser cavity in order to accommodate the constraints imposed by typical SCRAMJET combustion characteristics as well as to increase the instrument's velocity dynamic range to span an intra-image range in excess of 2 km/s. The various technical efforts were brought together in a series of experiments demonstrating the applicability of the technique in a high pressure, high temperature H2-air combustion system. The resultant images were compared with 2-D flow simulations in order to determine the accuracy of the instrument. Mean velocity imaging in flows with an axis of symmetry was demonstrated with an accuracy of +/- 50 m/s out of an intra-image dynamic range of 1600 m/s, including reversed flow. A more complex configuration amenable to single-shot imaging in flows without an axis of symmetry was also demonstrated. Limitations imposed by available equipment resulted in an accuracy of about +/- 200 m/s out of 1750 m/s in these demonstrations. Minor modifications to the present configuration were suggested to improve this performance. Each technical task is described in detail, along with significance of the results for the overall imaging velocimeter configuration. This report should allow the user community to integrate this new measurement capability in their existing instrumentation platforms.

Allen, M. G.↗
NASA NTRS1992

Image pattern recognition supporting interactive analysis and graphical visualization

Image Pattern Recognition attempts to infer properties of the world from image data. Such capabilities are crucial for making measurements from satellite or telescope images related to Earth and space science problems. Such measurements can be the required product itself, or the measurements can be used as input to a computer graphics system for visualization purposes. At present, the field of image pattern recognition lacks a unified scientific structure for developing and evaluating image pattern recognition applications. The overall goal of this project is to begin developing such a structure. This report summarizes results of a 3-year research effort in image pattern recognition addressing the following three principal aims: (1) to create a software foundation for the research and identify image pattern recognition problems in Earth and space science; (2) to develop image measurement operations based on Artificial Visual Systems; and (3) to develop multiscale image descriptions for use in interactive image analysis.

Coggins, James M.↗
NASA NTRS1995

Fine-resolution imaging of solar features using Phase-Diverse Speckle

Phase-diverse speckle (PDS) is a novel imaging technique intended to overcome the degrading effects of atmospheric turbulence on fine-resolution imaging. As its name suggests, PDS is a blend of phase-diversity and speckle-imaging concepts. PDS reconstructions on solar data were validated by simulation, by demonstrating internal consistency of PDS estimates, and by comparing PDS reconstructions with those produced from well accepted speckle-imaging processing. Several sources of error in data collected with the Swedish Vacuum Solar Telescope (SVST) were simulated: CCD noise, quantization error, image misalignment, and defocus error, as well as atmospheric turbulence model error. The simulations demonstrate that fine-resolution information can be reliably recovered out to at least 70% of the diffraction limit without significant introduction of image artifacts. Additional confidence in the SVST restoration is obtained by comparing its spatial power spectrum with previously-published power spectra derived from both space-based images and earth-based images corrected with traditional speckle-imaging techniques; the shape of the spectrum is found to match well the previous measurements. In addition, the imagery is found to be consistent with, but slightly sharper than, imagery reconstructed with accepted speckle-imaging techniques.

Paxman, Richard G.↗
NASA NTRS2000

The Extended-Image Tracking Technique Based on the Maximum Likelihood Estimation

This paper describes an extended-image tracking technique based on the maximum likelihood estimation. The target image is assume to have a known profile covering more than one element of a focal plane detector array. It is assumed that the relative position between the imager and the target is changing with time and the received target image has each of its pixels disturbed by an independent additive white Gaussian noise. When a rotation-invariant movement between imager and target is considered, the maximum likelihood based image tracking technique described in this paper is a closed-loop structure capable of providing iterative update of the movement estimate by calculating the loop feedback signals from a weighted correlation between the currently received target image and the previously estimated reference image in the transform domain. The movement estimate is then used to direct the imager to closely follow the moving target. This image tracking technique has many potential applications, including free-space optical communications and astronomy where accurate and stabilized optical pointing is essential.

Tsou, Haiping↗
NASA NTRS2000

High-Definition Television (HDTV) Images for Earth Observations and Earth Science Applications

As part of Detailed Test Objective 700-17A, astronauts acquired Earth observation images from orbit using a high-definition television (HDTV) camcorder, Here we provide a summary of qualitative findings following completion of tests during missions STS (Space Transport System)-93 and STS-99. We compared HDTV imagery stills to images taken using payload bay video cameras, Hasselblad film camera, and electronic still camera. We also evaluated the potential for motion video observations of changes in sunlight and the use of multi-aspect viewing to image aerosols. Spatial resolution and color quality are far superior in HDTV images compared to National Television Systems Committee (NTSC) video images. Thus, HDTV provides the first viable option for video-based remote sensing observations of Earth from orbit. Although under ideal conditions, HDTV images have less spatial resolution than medium-format film cameras, such as the Hasselblad, under some conditions on orbit, the HDTV image acquired compared favorably with the Hasselblad. Of particular note was the quality of color reproduction in the HDTV images HDTV and electronic still camera (ESC) were not compared with matched fields of view, and so spatial resolution could not be compared for the two image types. However, the color reproduction of the HDTV stills was truer than colors in the ESC images. As HDTV becomes the operational video standard for Space Shuttle and Space Station, HDTV has great potential as a source of Earth-observation data. Planning for the conversion from NTSC to HDTV video standards should include planning for Earth data archiving and distribution.

Robinson, Julie A.↗
NASA NTRS2000

The Proton and Electron Aurora as Seen by Image-FUV and FAST

The Far Ultraviolet (FUV) instrument on IMAGE images the aurora in three different wavelength regions. The Wideband Imaging Camera (WIC) observes the molecular Lyman-Birge-Hopfield (LBH) and atomic Nitrogen emissions at 140-190 nm. The two channels of the Spectrographic Imager (SI) observe the Doppler shifted Lyman Alpha emission of precipitating protons at 121.8 nm (SI 12) and the mostly electron produced emission from oxygen OI at 135.6 nm (SI13). The major advantage of FUV compared to previously flown UV imagers is the simultaneous operation of all three imaging systems without the need for filter changes and the uncertainty of temporal changes of the aurora between exposures. The FAST satellite passes every two hours through FUV images during apogee operations. This enables a comparison between the remotely imaged particle precipitation and the in-situ measurements along the FAST track. The detailed analysis of images from all three systems together with a full simulation of auroral emissions based on in-situ measurements by FAST confirms the laboratory calibrations of FUV. The spatial resolution and image quality of WIC permits the observation of a good correspondence between in-situ and remote measurements of precipitation boundaries. The clear separation between the lower latitude proton precipitation and the more structured higher latitude electron precipitation is obtained with both SI-channels.

Gerard, J.-C.↗
NASA NTRS1996

Perceptual Classification Images from Vernier Acuity Masked by Noise

Letting external noise rather than internal noise limit discrimination performance allows information to be extracted about the observer's stimulus classification rule. A perceptual classification image is the correlation over trials between the noise amplitude at a spatial location and the observer's responses. If, for example, the observer followed the rule of the ideal observer, the perceptual classification image would be an estimate of the ideal observer filter, the difference between the two unmasked images being discriminated. Perceptual classification images were estimated for a vernier discrimination task. The display screen had 48 pixels per degree horizontally and vertically. The no-offset image had a dark horizontal line of 4 pixels, a 1 pixel space, and 4 more dark pixels. Classification images were based on 1600 discrimination trials with the line contrast adjusted to keep the error rate near 25 percent. In the offset image, the second line was one pixel higher. Unlike the ideal observer filter (a horizontal dipole), the observer perceptual classification images are strongly oriented. Fourier transforms of the classification images had a peak amplitude near one cycle per degree and an orientation near 25 degrees. The spatial spread is much more than image blur predicts, and probably indicates the spatial position uncertainty in the task.

Ahumada, A. J.↗
NASA NTRS1991

Medical Imaging System

The MD Image System, a true-color image processing system that serves as a diagnostic aid and tool for storage and distribution of images, was developed by Medical Image Management Systems, Huntsville, AL, as a "spinoff from a spinoff." The original spinoff, Geostar 8800, developed by Crystal Image Technologies, Huntsville, incorporates advanced UNIX versions of ELAS (developed by NASA's Earth Resources Laboratory for analysis of Landsat images) for general purpose image processing. The MD Image System is an application of this technology to a medical system that aids in the diagnosis of cancer, and can accept, store and analyze images from other sources such as Magnetic Resonance Imaging.

Source record↗
NASA NTRS2004

Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

Decker, Arthur J.↗
NASA NTRS2008

Fpack and Funpack Utilities for FITS Image Compression and Uncompression

Fpack is a utility program for optimally compressing images in the FITS (Flexible Image Transport System) data format (see http://fits.gsfc.nasa.gov). The associated funpack program restores the compressed image file back to its original state (as long as a lossless compression algorithm is used). These programs may be run from the host operating system command line and are analogous to the gzip and gunzip utility programs except that they are optimized for FITS format images and offer a wider choice of compression algorithms. Fpack stores the compressed image using the FITS tiled image compression convention (see http://fits.gsfc.nasa.gov/fits_registry.html). Under this convention, the image is first divided into a user-configurable grid of rectangular tiles, and then each tile is individually compressed and stored in a variable-length array column in a FITS binary table. By default, fpack usually adopts a row-by-row tiling pattern. The FITS image header keywords remain uncompressed for fast access by FITS reading and writing software. The tiled image compression convention can in principle support any number of different compression algorithms. The fpack and funpack utilities call on routines in the CFITSIO library (http://hesarc.gsfc.nasa.gov/fitsio) to perform the actual compression and uncompression of the FITS images, which currently supports the GZIP, Rice, H-compress, and PLIO IRAF pixel list compression algorithms.

Pence, W.↗
NASA NTRS2009

Improving 3D Wavelet-Based Compression of Hyperspectral Images

Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a manner similar to that of a baseline hyperspectral- image-compression method. The mean values are encoded in the compressed bit stream and added back to the data at the appropriate decompression step. The overhead incurred by encoding the mean values only a few bits per spectral band is negligible with respect to the huge size of a typical hyperspectral data set. The other method is denoted modified decomposition. This method is so named because it involves a modified version of a commonly used multiresolution wavelet decomposition, known in the art as the 3D Mallat decomposition, in which (a) the first of multiple stages of a 3D wavelet transform is applied to the entire dataset and (b) subsequent stages are applied only to the horizontally-, vertically-, and spectrally-low-pass subband from the preceding stage. In the modified decomposition, in stages after the first, not only is the spatially-low-pass, spectrally-low-pass subband further decomposed, but also spatially-low-pass, spectrally-high-pass subbands are further decomposed spatially. Either method can be used alone to improve the quality of a reconstructed image (see figure). Alternatively, the two methods can be combined by first performing modified decomposition, then subtracting the mean values from spatial planes of spatially-low-pass subbands.

Klimesh, Matthew↗
NASA NTRS2008

Software for Verifying Image-Correlation Tie Points

A computer program enables assessment of the quality of tie points in the image-correlation processes of the software described in the immediately preceding article. Tie points are computed in mappings between corresponding pixels in the left and right images of a stereoscopic pair. The mappings are sometimes not perfect because image data can be noisy and parallax can cause some points to appear in one image but not the other. The present computer program relies on the availability of a left- right correlation map in addition to the usual right left correlation map. The additional map must be generated, which doubles the processing time. Such increased time can now be afforded in the data-processing pipeline, since the time for map generation is now reduced from about 60 to 3 minutes by the parallelization discussed in the previous article. Parallel cluster processing time, therefore, enabled this better science result. The first mapping is typically from a point (denoted by coordinates x,y) in the left image to a point (x',y') in the right image. The second mapping is from (x',y') in the right image to some point (x",y") in the left image. If (x,y) and(x",y") are identical, then the mapping is considered perfect. The perfect-match criterion can be relaxed by introducing an error window that admits of round-off error and a small amount of noise. The mapping procedure can be repeated until all points in each image not connected to points in the other image are eliminated, so that what remains are verified correlation data.

Klimeck, Gerhard↗
NASA NTRS2009

The Hurricane Imaging Radiometer (HIRAD): Instrument Status and Performance Predictions

The Hurricane Imaging Radiometer (HIRAD) is an innovative radiometer which offers new and unique remotely sensed observations of both extreme oceanic wind events and strong precipitation. It is based on the airborne Stepped Frequency Microwave Radiometer (SFMR) [Uhlhorn and Black, 2004]. The HIRAD instrument advances beyond the current nadir viewing SFMR to an equivalent wide-swath SFMR imager using passive microwave synthetic thinned aperture radiometer (STAR) technology [Ruf et al., 1988]. This sensor operates over 4-7 GHz, where the required tropical cyclone remote sensing physics has been validated by both SFMR and WindSat radiometer [Bettenhausen et al., 2006; Brown et al., 2006]. HIRAD incorporates a new and unique array antenna design along with several technologies successfully demonstrated by the Lightweight Rain Radiometer instrument [Ruf et al., 2002; Ruf and Principe, 2003]. HIRAD will be a compact, lightweight, low-power instrument with no moving parts that will produce wide-swath imagery of ocean winds and rain in hurricane conditions. Accurate observations of surface ocean vector winds (OVW) with high spatial and temporal resolution are required for understanding and predicting tropical cyclones. The Hurricane Imaging Radiometer (HIRAD) is an innovative architecture which offers new and unique remotely sensed observations of both extreme oceanic wind events and strong precipitation. It is based on the airborne Stepped Frequency Microwave Radiometer (SFMR), which is a proven remote sensing technique for observing tropical cyclone (TC) ocean surface wind speeds and rain rates. The proposed HIRAD instrument advances beyond the current nadir viewing SFMR to an equivalent wide-swath SFMR imager using passive microwave synthetic thinned aperture radiometer (STAR) technology combined with a a unique array antenna design. The overarching design concept of HIRAD is to combine the multi-frequency C-band observing strategy of the SFMR with STAR technology to produce a wide-swath imager. Single frequency STAR technology The Hurricane Imaging Radiometer (HIRAD) is an innovative radiometer which offers new and unique remotely sensed observations of both extreme oceanic wind events and strong precipitation. It is based on the airborne Stepped Frequency Microwave Radiometer (SFMR) [Uhlhorn and Black, 2004]. The HIRAD instrument advances beyond the current nadir viewing SFMR to an equivalent wide-swath SFMR imager using passive microwave synthetic thinned aperture radiometer (STAR) technology [Ruf et al., 1988]. This sensor operates over 4-7 GHz, where the required tropical cyclone remote sensing physics has been validated by both SFMR and WindSat radiometer [Bettenhausen et al., 2006; Brown et al., 2006]. HIRAD incorporates a new and unique array antenna design along with several technologies successfully demonstrated by the Lightweight Rain Radiometer instrument [Ruf et al., 2002; Ruf and Principe, 2003]. HIRAD will be a compact, lightweight, low-power instrument with no moving parts that will produce wide-swath imagery of ocean winds and rain in hurricane conditions. Accurate observations of surface ocean vector winds (OVW) with high spatial and temporal resolution are required for understanding and predicting tropical cyclones. The Hurricane Imaging Radiometer (HIRAD) is an innovative architecture which offers new and unique remotely sensed observations of both extreme oceanic wind events and strong precipitation. It is based on the airborne Stepped Frequency Microwave Radiometer (SFMR), which is a proven remote sensing technique for observing tropical cyclone (TC) ocean surface wind speeds and rain rates. The proposed HIRAD instrument advances beyond the current nadir viewing SFMR to an equivalent wide-swath SFMR imager using passive microwave synthetic thinned aperture radiometer (STAR) technology combined with a a unique array antenna design. The overarching design concept of HIRAD is to combine the multi-frequency C-banbserving strategy of the SFMR with STAR technology to produce a wide-swath imager. Single frequency STAR technology

Ruf, Christopher↗
NASA NTRS2009

Flightspeed Integral Image Analysis Toolkit

The Flightspeed Integral Image Analysis Toolkit (FIIAT) is a C library that provides image analysis functions in a single, portable package. It provides basic low-level filtering, texture analysis, and subwindow descriptor for applications dealing with image interpretation and object recognition. Designed with spaceflight in mind, it addresses: Ease of integration (minimal external dependencies) Fast, real-time operation using integer arithmetic where possible (useful for platforms lacking a dedicated floatingpoint processor) Written entirely in C (easily modified) Mostly static memory allocation 8-bit image data The basic goal of the FIIAT library is to compute meaningful numerical descriptors for images or rectangular image regions. These n-vectors can then be used directly for novelty detection or pattern recognition, or as a feature space for higher-level pattern recognition tasks. The library provides routines for leveraging training data to derive descriptors that are most useful for a specific data set. Its runtime algorithms exploit a structure known as the "integral image." This is a caching method that permits fast summation of values within rectangular regions of an image. This integral frame facilitates a wide range of fast image-processing functions. This toolkit has applicability to a wide range of autonomous image analysis tasks in the space-flight domain, including novelty detection, object and scene classification, target detection for autonomous instrument placement, and science analysis of geomorphology. It makes real-time texture and pattern recognition possible for platforms with severe computational restraints. The software provides an order of magnitude speed increase over alternative software libraries currently in use by the research community. FIIAT can commercially support intelligent video cameras used in intelligent surveillance. It is also useful for object recognition by robots or other autonomous vehicles

Thompson, David R.↗
NASA NTRS2007

Automated Recognition of 3D Features in GPIR Images

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Park, Han↗
NASA NTRS2010

Image Segmentation Analysis for NASA Earth Science Applications

NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.

Tilton, James C.↗
NASA NTRS2012

Development of Two Color Fluorescent Imager and Integrated Fluidic System for Nanosatellite Biology Applications

Nanosatellites offer frequent, low-cost space access as secondary payloads on launches of larger conventional satellites. We summarize the payload science and technology of the Microsatellite in-situ Space Technologies (MisST) nanosatellite for conducting automated biological experiments. The payload (two fused 10-cm cubes) includes 1) an integrated fluidics system that maintains organism viability and supports growth and 2) a fixed-focus imager with fluorescence and scattered-light imaging capabilities. The payload monitors temperature, pressure and relative humidity, and actively controls temperature. C. elegans (nematode, 50 m diameter x 1 mm long) was selected as a model organism due to previous space science experience, its completely sequenced genome, size, hardiness, and the variety of strains available. Three strains were chosen: two green GFP-tagged strains and one red tdTomato-tagged strain that label intestinal, nerve, and pharyngeal cells, respectively. The integrated fluidics system includes bioanalytical and reservoir modules. The former consists of four 150 L culture wells and a 4x5 mm imaging zone the latter includes two 8 mL fluid reservoirs for reagent and waste storage. The fluidic system is fabricated using multilayer polymer rapid prototyping: laser cutting, precision machining, die cutting, and pressure-sensitive adhesives it also includes eight solenoid-operated valves and one mini peristaltic pump. Young larval-state (L2) nematodes are loaded in C. elegans Maintenance Media (CeMM) in the bioanalytical module during pre-launch assembly. By the time orbit is established, the worms have grown to sufficient density to be imaged and are fed fresh CeMM. The strains are pumped sequentially into the imaging area, imaged, then pumped into waste. Reagent storage utilizes polymer bags under slight pressure to prevent bubble formation in wells or channels. The optical system images green and red fluorescence bands by excitation with blue (473 nm peak) and amber (587 nm peak) LEDs it achieves 8 m lateral resolution using a CMOS imaging chip (as configured for serial data speeds) or 4 m resolution using USB imaging chips. The imager consists of a modified commercial off-the-shelf CMOS chip camera, amber, blue and white LEDs, as well as a relay lens and dual-band filters to obviate moving parts while supporting both fluorescence wavelengths.

Wu, Diana Terri↗
NASA NTRS2015

Nonnegative Matrix Factorization for Efficient Hyperspectral Image Projection

Hyperspectral imaging for remote sensing has prompted development of hyperspectral image projectors that can be used to characterize hyperspectral imaging cameras and techniques in the lab. One such emerging astronomical hyperspectral imaging technique is wide-field double-Fourier interferometry. NASA's current, state-of-the-art, Wide-field Imaging Interferometry Testbed (WIIT) uses a Calibrated Hyperspectral Image Projector (CHIP) to generate test scenes and provide a more complete understanding of wide-field double-Fourier interferometry. Given enough time, the CHIP is capable of projecting scenes with astronomically realistic spatial and spectral complexity. However, this would require a very lengthy data collection process. For accurate but time-efficient projection of complicated hyperspectral images with the CHIP, the field must be decomposed both spectrally and spatially in a way that provides a favorable trade-off between accurately projecting the hyperspectral image and the time required for data collection. We apply nonnegative matrix factorization (NMF) to decompose hyperspectral astronomical datacubes into eigenspectra and eigenimages that allow time-efficient projection with the CHIP. Included is a brief analysis of NMF parameters that affect accuracy, including the number of eigenspectra and eigenimages used to approximate the hyperspectral image to be projected. For the chosen field, the normalized mean squared synthesis error is under 0.01 with just 8 eigenspectra. NMF of hyperspectral astronomical fields better utilizes the CHIP's capabilities, providing time-efficient and accurate representations of astronomical scenes to be imaged with the WIIT.

NMF↗