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NASA NTRS1992

Diversity imaging techniques in lidar

Diversity imaging techniques have been successfully employed in conventional microwave range-Doppler imaging radars to obtain high resolution images of both natural and man-made targets. These techniques allow microwave radars to achieve image resolution which would otherwise require excessively large antennas. Recent advances in coherent laser radar techniques and signal processing have led to the development of range-Doppler imaging laser radars. While much of the theory and signal processing techniques used in microwave radars can be brought to bear on laser radars, the significant difference in wavelength results in issues peculiar to laser radar systems. Both the fundamental concepts and specific applications of diversity imaging techniques applied to laser radar imaging systems will be discussed. Angle, frequency, and bistatic angle degrees of freedom can be employed in a coherent laser radar imaging system to achieve image resolution which exceeds the traditional Rayleigh criterion associated with the receive aperture. In diversity imaging, angle and frequency degrees of freedom can be used to synthesize an effective aperture providing range and Doppler target information. The ability to vary the bistatic angle provides an additional means of synthesizing an effective aperture. Both simulated and experimentally obtained laser radar images of spinning and/or tumbling objects utilizing both angular and frequency diversity will be presented. In coherent laser radar systems, image quality can be dominated by laser speckle effects. In particular, the signal-to-noise ratio (SNR) of a coherent laser radar image is at most unity in the presence of fully developed speckle. Diversity techniques can be utilized to improve the image SNR; simple incoherent averaging of images utilizing temporal and polarization degrees of freedom can significantly improve image SNR. Both the SNR and image resolution (as defined by the synthetic aperture) contribute to image quality. The relationship between the available degrees of freedom and image quality are discussed.

Schultz, K. I.↗

NASA NTRS2008

Symmetric Phase-Only Filtering in Particle-Image Velocimetry

Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes. This scheme yields optimal performance because the amounts of normalization applied to the spatial-frequency contents of the input and filter scenes are just enough to enhance their high-spatial-frequency contents while reducing their spurious low-spatial-frequency content. As a result, in SPOF PIV processing, particle-displacement correlation peaks can readily be detected above spurious background peaks, without need for masking or background subtraction.

Wemet, Mark P.↗

NASA NTRS2012

Smart Image Enhancement Process

Contrast and lightness measures are used to first classify the image as being one of non-turbid and turbid. If turbid, the original image is enhanced to generate a first enhanced image. If non-turbid, the original image is classified in terms of a merged contrast/lightness score based on the contrast and lightness measures. The non-turbid image is enhanced to generate a second enhanced image when a poor contrast/lightness score is associated therewith. When the second enhanced image has a poor contrast/lightness score associated therewith, this image is enhanced to generate a third enhanced image. A sharpness measure is computed for one image that is selected from (i) the non-turbid image, (ii) the first enhanced image, (iii) the second enhanced image when a good contrast/lightness score is associated therewith, and (iv) the third enhanced image. If the selected image is not-sharp, it is sharpened to generate a sharpened image. The final image is selected from the selected image and the sharpened image.

Jobson, Daniel J.↗

NASA NTRS2008

Hyperspectral Fluorescence and Reflectance Imaging Instrument

The system is a single hyperspectral imaging instrument that has the unique capability to acquire both fluorescence and reflectance high-spatial-resolution data that is inherently spatially and spectrally registered. Potential uses of this instrument include plant stress monitoring, counterfeit document detection, biomedical imaging, forensic imaging, and general materials identification. Until now, reflectance and fluorescence spectral imaging have been performed by separate instruments. Neither a reflectance spectral image nor a fluorescence spectral image alone yields as much information about a target surface as does a combination of the two modalities. Before this system was developed, to benefit from this combination, analysts needed to perform time-consuming post-processing efforts to co-register the reflective and fluorescence information. With this instrument, the inherent spatial and spectral registration of the reflectance and fluorescence images minimizes the need for this post-processing step. The main challenge for this technology is to detect the fluorescence signal in the presence of a much stronger reflectance signal. To meet this challenge, the instrument modulates artificial light sources from ultraviolet through the visible to the near-infrared part of the spectrum; in this way, both the reflective and fluorescence signals can be measured through differencing processes to optimize fluorescence and reflectance spectra as needed. The main functional components of the instrument are a hyperspectral imager, an illumination system, and an image-plane scanner. The hyperspectral imager is a one-dimensional (line) imaging spectrometer that includes a spectrally dispersive element and a two-dimensional focal plane detector array. The spectral range of the current imaging spectrometer is between 400 to 1,000 nm, and the wavelength resolution is approximately 3 nm. The illumination system consists of narrowband blue, ultraviolet, and other discrete wavelength light-emitting-diode (LED) sources and white-light LED sources designed to produce consistently spatially stable light. White LEDs provide illumination for the measurement of reflectance spectra, while narrowband blue and UV LEDs are used to excite fluorescence. Each spectral type of LED can be turned on or off depending on the specific remote-sensing process being performed. Uniformity of illumination is achieved by using an array of LEDs and/or an integrating sphere or other diffusing surface. The image plane scanner uses a fore optic with a field of view large enough to provide an entire scan line on the image plane. It builds up a two-dimensional image in pushbroom fashion as the target is scanned across the image plane either by moving the object or moving the fore optic. For fluorescence detection, spectral filtering of a narrowband light illumination source is sometimes necessary to minimize the interference of the source spectrum wings with the fluorescence signal. Spectral filtering is achieved with optical interference filters and absorption glasses. This dual spectral imaging capability will enable the optimization of reflective, fluorescence, and fused datasets as well as a cost-effective design for multispectral imaging solutions. This system has been used in plant stress detection studies and in currency analysis.

Ryan, Robert E.↗

NASA NTRS2011

Geometric registration of images by similarity transformation using two reference points

A method for registering a first image to a second image using a similarity transformation. The each image includes a plurality of pixels. The first image pixels are mapped to a set of first image coordinates and the second image pixels are mapped to a set of second image coordinates. The first image coordinates of two reference points in the first image are determined. The second image coordinates of these reference points in the second image are determined. A Cartesian translation of the set of second image coordinates is performed such that the second image coordinates of the first reference point match its first image coordinates. A similarity transformation of the translated set of second image coordinates is performed. This transformation scales and rotates the second image coordinates about the first reference point such that the second image coordinates of the second reference point match its first image coordinates.

Kang, Yong Q.↗

NASA NTRSDate not supplied

Imaging Science Requirements for a Uranus Flagship Mission

Introduction: Our presentation will summarize the requirements for UV/Visible/Near-IR remote-sensing imaging science instruments (including mapping spectrometers) envisioned for the Uranus Orbiter and Probe (UOP) mission recommended by the recent planetary decadal survey. The Uranus system offers many targets for imaging investigations. Imaging science investigations are crucial to answering fundamental questions about various physical processes that shape the Uranian system and drive its evolution. Each science objective addressed through imaging has requirements for observation and illumination geometries as well as coverage and resolution in the spatial, spectral and temporal dimensions. By combining the requirements for the diverse science targets in the Uranian system, we present a set of overarching imaging science requirements for UOP. Atmospheric Science: The study of Uranus’ atmosphere is crucial in understanding the planet's energy balance and interior heat flux. Imaging observations map how the atmosphere scatters incident solar radiation. Imaging is key to characterizing planetary-scale energy/material transport though global atmospheric dynamics and local meteorology. Hyperspectral mapping combined with radiative transfer analysis reveals the vertical distribution of clouds and hazes. Chronicling temporal variabilities of atmospheric features is key to understanding the various processes that operate within the dynamic Uranian atmosphere. A comprehensive understanding of Uranus' atmosphere is essential in deciphering the planet's origins and evolution as well as providing insight into the broader field of planetary and exoplanetary science. Magnetospheric Interactions: Imaging science is critical to understand the elusive Uranian aurorae. Mapping the aurorae and their temporal dynamics will reveal the interaction of Uranus’ complex magnetosphere with the solar wind. The apparent decades-long cooling of Uranus’ upper atmosphere is thought to be due to changes in energy input from the overlying magnetosphere, and so mapping and quantifying the auroral evidence for this energy flux is particularly relevant for upper atmospheric science In addition, Uranus’ large moons may generate auroral footprints, which can be used to constrain the planet’s unusual magnetic field structure. Satellite Interiors: Imaging science can address the potential habitability of the larger moons of Uranus by measuring their shapes and rotational states (including librations), which combined with gravity data are crucial to understanding their internal density structure. Satellite Origins: Origin markers such as organics and volatiles may be preserved on the satellite surfaces, which may be sensed by UV/IR mapping. Imaging combined with the moons’ densities may reveal whether the moons formed in Uranus’ circumplanetary disk, rings, or from ejecta of a giant impact on Uranus. Satellite Geology: Imaging investigations, including stereo imaging to obtain topography, will enable constraining each satellite’s geologic history by investigating the distributions and morphologies of impact craters, tectonic and potential cryovolcanic features, and regolith and mass wasting deposits. These records will help unravel the past complex history of orbital resonances that occurred between Uranian satellites, which may have driven geologic activity. Analyses of these features will also point to resurfacing styles and modification processes important for understanding each moon’s thermal evolution, and any present-day geological activity, including potential subsurface deep oceans or possible current activity. Additionally, hyperspectral imaging will enable mapping the surface composition and identifying the potential presence of material that may point to a subsurface ocean, such as ammonia-bearing species. Rings and Small Moons: Imaging observations elucidate the physical processes that contribute to the complex ring-moon system’s inferred unstable state. Imaging surveys will seek smaller moons and other debris that could provide evidence of past collisions, while precise astrometric data of moons will determine the current dynamical state of the system. Many of Uranus' rings are exceptionally narrow, and images of the rings covering a broad range of longitudes, and high-resolution images of features within the rings will reveal how these rings are confined and maintained, as well as illustrating the rings' internal structure and dynamics. Images of the rings and moons obtained at a variety of wavelengths and lighting conditions are also needed to quantify the composition and the size distribution of the small satellites and ring particles.

K M Sayanagi↗

NASA NTRS1996

Visualization of High Latitude Ion Upflow in Support of the Image Mission

The study of the magnetosphere is a 400 year old science that began with the publication by Gilbert, in 1600, of his hypotheses that the Earth was a giant magnet. Since then we have learned many things about the magnetosphere, particularly in the last 40 years of the space age, but we still have many unanswered questions. In spite of the many thousands of observations of this system we still lack a global understanding of how it works. This is due to its large size and tenuous nature that mean that any measurement made of the fields or particles involved only give one a knowledge of the local conditions at a given time. To gain a global perspective through such observations would require the simultaneous operation of thousands of satellites spread throughout the magnetospheric system in addition to observations made on the ground. Such a program would be impractical at least from financial considerations. What is needed for the advancement of magnetospheric physics is to develop the same capabilities that astrophysicists, solar physicists and meteorologists have been using for years --- the ability to stand back from the object under study and see it in its entirety. The challenge for doing this for the magnetosphere is that the particle densities are very low and the material is, for the most part, not luminous. In the last 25 years several ideas have been proposed that would allow at least the imaging of certain portions of the magnetosphere. These include imaging of the plasmasphere through the resonant scattering of solar 304 A from He+ ions, imaging of various hot plasma populations (i.e. the ring current, plasmasheet, upflowing ionospheric ions, etc.) from the neutral atoms that result when ions of these populations charge exchange with the hydrogen geocorona, and imaging the aurora at various wavelengths in the far ultraviolet. In addition, a novel technique for probing various boundaries in the magnetosphere by bouncing low frequency radio waves off of them has been extensively studied. Such a technique is analogous to the way the under water world can be probed with sonar. About five years ago NASA convened a science working group to study the possibility of flying a magnetospheric imaging mission. This resulted in a number of proposals for such a mission, one of which was selected to be the first MIDEX mission, to be launched in early 2000. The mission is called IMAGE (Imager for Magnetopause to Aurora Global Exploration) and its P.I. is J. Burch at SwRI. The IMAGE spacecraft will carry imagers to view the plasmasphere, aurora, ring current, inner plasmasheet, and upflowing ionospheric ions as well as a radio sounder to probe the location, shape and dynamics of the magnetopause, plasmapause, etc. Between its selection last April and the non advocacy mission review, which takes place next spring, the IMAGE teams needs to further refine the design of the mission and its instruments. The theory and modeling (T&M) subgroup of this team has the task of demonstrating what kind of images the instruments on IMAGE will see as well as showing that useful scientific information can be extracted from such images. As a central element to the efforts of the T&M subgroup we have decided to simulate and create synthetic images for the magnetic cloud event of October, 1995. In this event a large cloud, with high plasma densities and strong magnetic fields, ejected from the sun collided with the earth's magnetosphere triggering a three day period of intense magnetic storms and substorms. This event was observed from a number of different spacecraft and on the ground so we have a good data set to work with. In our work we will place the IMAGE spacecraft in the magnetosphere on its proposed orbit, with its proposed instruments, to see what it would see had it been there. Existing models of the plasmasphere, ring current and magnetopause will be run for this event to give the structures for the imaging instruments. There are several models which are lacking and which need to be developed. These include a model for the cusp, the inner plasmasheet and the upflowing ions. My task this summer was to develop the upflowing ion model and use it to create synthetic images.

Wilson, Gordon R.↗

NASA NTRS2003

The Athena Microscopic Imager Investigation

The Athena science payload on the Mars Exploration Rovers (MER) includes the Microscopic Imager (MI) [1]. The MI is a fixed-focus camera mounted on the end of an extendable instrument arm, the Instrument Deployment Device (IDD; see Figure 1).The MI was designed to acquire images at a spatial resolution of 30 microns/pixel over a broad spectral range (400 - 700 nm; see Table 1). Technically, the microscopic imager is not a microscope: it has a fixed magnification of 0.4 and is intended to produce images that simulate a geologist s view through a common hand lens. In photographers parlance, the system makes use of a macro lens. The MI uses the same electronics design as the other MER cameras [2, 3] but has optics that yield a field of view of 31 31 mm across a 1024 1024 pixel CCD image (Figure 2). The MI acquires images using only solar or skylightillumination of the target surface. A contact sensor is used to place the MI slightly closer to the target surface than its best focus distance (about 66 mm), allowing concave surfaces to be imaged in good focus. Because the MI has a relatively small depth of field (3 mm), a single MI image of a rough surface will contain both focused and unfocused areas. Coarse focusing will be achieved by moving the IDD away from a rock target after the contact sensor is activated. Multiple images taken at various distances will be acquired to ensure good focus on all parts of rough surfaces. By combining a set of images acquired in this way, a completely focused image can be assembled. Stereoscopic observations can be obtained by moving the MI laterally relative to its boresight. Estimates of the position and orientation of the MI for each acquired image will be stored in the rover computer and returned to Earth with the image data. The MI optics will be protected from the Martian environment by a retractable dust cover. The dust cover includes a Kapton window that is tinted orange to restrict the spectral bandpass to 500-700 nm, allowing color information to be obtained by taking images with the dust cover open and closed. The MI will image the same materials measured by other Athena instruments (including surfaces prepared by the Rock Abrasion Tool), as well as rock and soil targets of opportunity. Subsets of the full image array can be selected and/or pixels can be binned to reduce data volume. Image compression will be used to maximize the information contained in the data returned to Earth. The resulting MI data will place other MER instrument data in context and aid in petrologic and geologic interpretations of rocks and soils on Mars.

K E Herkenhoff↗

NASA NTRS2002

Contrast and harmonic imaging improves accuracy and efficiency of novice readers for dobutamine stress echocardiography

BACKGROUND: Newer contrast agents as well as tissue harmonic imaging enhance left ventricular (LV) endocardial border delineation, and therefore, improve LV wall-motion analysis. Interpretation of dobutamine stress echocardiography is observer-dependent and requires experience. This study was performed to evaluate whether these new imaging modalities would improve endocardial visualization and enhance accuracy and efficiency of the inexperienced reader interpreting dobutamine stress echocardiography. METHODS AND RESULTS: Twenty-nine consecutive patients with known or suspected coronary artery disease underwent dobutamine stress echocardiography. Both fundamental (2.5 MHZ) and harmonic (1.7 and 3.5 MHZ) mode images were obtained in four standard views at rest and at peak stress during a standard dobutamine infusion stress protocol. Following the noncontrast images, Optison was administered intravenously in bolus (0.5-3.0 ml), and fundamental and harmonic images were obtained. The dobutamine echocardiography studies were reviewed by one experienced and one inexperienced echocardiographer. LV segments were graded for image quality and function. Time for interpretation also was recorded. Contrast with harmonic imaging improved the diagnostic concordance of the novice reader to the expert reader by 7.1%, 7.5%, and 12.6% (P < 0.001) as compared with harmonic imaging, fundamental imaging, and fundamental imaging with contrast, respectively. For the novice reader, reading time was reduced by 47%, 55%, and 58% (P < 0.005) as compared with the time needed for fundamental, fundamental contrast, and harmonic modes, respectively. With harmonic imaging, the image quality score was 4.6% higher (P < 0.001) than for fundamental imaging. Image quality scores were not significantly different for noncontrast and contrast images. CONCLUSION: Harmonic imaging with contrast significantly improves the accuracy and efficiency of the novice dobutamine stress echocardiography reader. The use of harmonic imaging reduces the frequency of nondiagnostic wall segments.

Non-NASA Center↗

NASA NTRS2009

Synthetic Foveal Imaging Technology

Synthetic Foveal imaging Technology (SyFT) is an emerging discipline of image capture and image-data processing that offers the prospect of greatly increased capabilities for real-time processing of large, high-resolution images (including mosaic images) for such purposes as automated recognition and tracking of moving objects of interest. SyFT offers a solution to the image-data processing problem arising from the proposed development of gigapixel mosaic focal-plane image-detector assemblies for very wide field-of-view imaging with high resolution for detecting and tracking sparse objects or events within narrow subfields of view. In order to identify and track the objects or events without the means of dynamic adaptation to be afforded by SyFT, it would be necessary to post-process data from an image-data space consisting of terabytes of data. Such post-processing would be time-consuming and, as a consequence, could result in missing significant events that could not be observed at all due to the time evolution of such events or could not be observed at required levels of fidelity without such real-time adaptations as adjusting focal-plane operating conditions or aiming of the focal plane in different directions to track such events. The basic concept of foveal imaging is straightforward: In imitation of a natural eye, a foveal-vision image sensor is designed to offer higher resolution in a small region of interest (ROI) within its field of view. Foveal vision reduces the amount of unwanted information that must be transferred from the image sensor to external image-data-processing circuitry. The aforementioned basic concept is not new in itself: indeed, image sensors based on these concepts have been described in several previous NASA Tech Briefs articles. Active-pixel integrated-circuit image sensors that can be programmed in real time to effect foveal artificial vision on demand are one such example. What is new in SyFT is a synergistic combination of recent advances in foveal imaging, computing, and related fields, along with a generalization of the basic foveal-vision concept to admit a synthetic fovea that is not restricted to one contiguous region of an image.

Hoenk, Michael↗

NASA NTRS2012

Computational Ghost Imaging for Remote Sensing

This work relates to the generic problem of remote active imaging; that is, a source illuminates a target of interest and a receiver collects the scattered light off the target to obtain an image. Conventional imaging systems consist of an imaging lens and a high-resolution detector array [e.g., a CCD (charge coupled device) array] to register the image. However, conventional imaging systems for remote sensing require high-quality optics and need to support large detector arrays and associated electronics. This results in suboptimal size, weight, and power consumption. Computational ghost imaging (CGI) is a computational alternative to this traditional imaging concept that has a very simple receiver structure. In CGI, the transmitter illuminates the target with a modulated light source. A single-pixel (bucket) detector collects the scattered light. Then, via computation (i.e., postprocessing), the receiver can reconstruct the image using the knowledge of the modulation that was projected onto the target by the transmitter. This way, one can construct a very simple receiver that, in principle, requires no lens to image a target. Ghost imaging is a transverse imaging modality that has been receiving much attention owing to a rich interconnection of novel physical characteristics and novel signal processing algorithms suitable for active computational imaging. The original ghost imaging experiments consisted of two correlated optical beams traversing distinct paths and impinging on two spatially-separated photodetectors: one beam interacts with the target and then illuminates on a single-pixel (bucket) detector that provides no spatial resolution, whereas the other beam traverses an independent path and impinges on a high-resolution camera without any interaction with the target. The term ghost imaging was coined soon after the initial experiments were reported, to emphasize the fact that by cross-correlating two photocurrents, one generates an image of the target. In CGI, the measurement obtained from the reference arm (with the high-resolution detector) is replaced by a computational derivation of the measurement-plane intensity profile of the reference-arm beam. The algorithms applied to computational ghost imaging have diversified beyond simple correlation measurements, and now include modern reconstruction algorithms based on compressive sensing.

Erkmen, Baris I.↗

NASA NTRS1992

Registering and resampling images in STSDAS

Registering different images can be difficult, especially if the images to be registered are images at different wavelengths, where features in one image may look entirely different or be absent from the second image. Using two new packages soon to be added to the STSDAS package, REGISTER and RESAMPLE, this job is done automatically. The REGISTER package allows the user to determine the amount of translation, rotation, and/or magnification needed to make two images, spectra, or time series congruent. The methods implemented to compute the registration parameters use: a set of the pixel coordinates of the same features identified in two files, or the FITS coordinate transformation parameters in the headers of two data files, or a single feature identified as the peak of a cross-correlation between two vectors. The coefficients describing the registration are defined by the equations (for a two-dimensional image): x = a + bx + cy, y = d + ex + fy, where (X,Y) are the pixel coordinates of a feature in the reference image, (x,y) are the pixel coordinates of a feature in the secondary image, and the computed coefficients are a, b, c, d, e, and f. Results may be produced by linking the output of REGISTER to RESAMPLE in a command language procedure. The output from REGISTER and the input to RESAMPLE consists of a matrix of coefficients (a through f above) fully specifying the registration. The RESAMPLE package resamples simple vector or image data for a given amount of translation, rotation (images only), and magnification, or reflection of the science data. Specific options included are: image rotation about the FITS reference pixel; scale changes, i.e. magnification or demagnification (for images, independently on both axes); simple translation; reflection (for images, about one or both axes); and resampling and registration to a reference data set. Output from the RESAMPLE task is the resampled image which may then be displayed and compared with the reference image.

Williamson, R. L., II↗

NASA NTRS1993

Imaging spectrometry of the Earth and other solar system bodies

Imaging spectrometry is a relatively new tool for remote sensing of the Earth and other bodies of the solar system. The technique dates back to the late 1970's and early 1980's. It is a natural extension of the earlier multi-spectral imagers developed for remote sensing that acquire images in a few, usually broad spectral bands. Imaging spectrometers combine aspects of classical spectrometers and imaging systems, making it possible to acquire literally hundreds of images of an object, each image in a separate, narrow spectral band. It is thus possible to perform spectroscopy on a pixel-by-pixel basis with the data acquired with an imaging spectrometer. Two imaging spectrometers have flown in space and several others are planned for future Earth and planetary missions. The French-built Phobos Infrared Spectrometer (ISM) was part of the payload of the Soviet Mars mission in 1988, and the JPL-built Near Infrared Mapping Spectrometer (NIMS) is currently en route to Jupiter aboard the Galileo spacecraft. Several airborne imaging spectrometers have been built in the past decade including the JPL-built Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) which is the only such sensor that covers the full solar reflected portion of the spectrum in narrow, contiguous spectral bands. NASA plans two imaging spectrometers for its Earth Observing System, the Moderate and the High Resolution Imaging Spectrometers (MODIS and HIRIS). A brief overview of the applications of imaging spectrometry to Earth science will be presented to illustrate the value of the tool to remote sensing and indicate the types of measurements that are required. The system design for AVIRS and a planetary imaging spectrometer will be presented to illustrate the engineering considerations and challenges that must be met in building such instruments. Several key sensor technology areas will be discussed in which miniaturization and/or enhanced performance through micromachining and nanofabrication may allow smaller, more robust, and more capable imaging spectrometers to be built in the future.

Vane, Gregg↗

NASA NTRS2011

Java Image I/O for VICAR, PDS, and ISIS

This library, written in Java, supports input and output of images and metadata (labels) in the VICAR, PDS image, and ISIS-2 and ISIS-3 file formats. Three levels of access exist. The first level comprises the low-level, direct access to the file. This allows an application to read and write specific image tiles, lines, or pixels and to manipulate the label data directly. This layer is analogous to the C-language "VICAR Run-Time Library" (RTL), which is the image I/O library for the (C/C++/Fortran) VICAR image processing system from JPL MIPL (Multimission Image Processing Lab). This low-level library can also be used to read and write labeled, uncompressed images stored in formats similar to VICAR, such as ISIS-2 and -3, and a subset of PDS (image format). The second level of access involves two codecs based on Java Advanced Imaging (JAI) to provide access to VICAR and PDS images in a file-format-independent manner. JAI is supplied by Sun Microsystems as an extension to desktop Java, and has a number of codecs for formats such as GIF, TIFF, JPEG, etc. Although Sun has deprecated the codec mechanism (replaced by IIO), it is still used in many places. The VICAR and PDS codecs allow any program written using the JAI codec spec to use VICAR or PDS images automatically, with no specific knowledge of the VICAR or PDS formats. Support for metadata (labels) is included, but is format-dependent. The PDS codec, when processing PDS images with an embedded VIAR label ("dual-labeled images," such as used for MER), presents the VICAR label in a new way that is compatible with the VICAR codec. The third level of access involves VICAR, PDS, and ISIS Image I/O plugins. The Java core includes an "Image I/O" (IIO) package that is similar in concept to the JAI codec, but is newer and more capable. Applications written to the IIO specification can use any image format for which a plug-in exists, with no specific knowledge of the format itself.

Deen, Robert G.↗

NASA NTRS2016

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

In developing software for independent verification and validation (IVV) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.

stair-step artifact↗

NASA NTRS2016

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

image registration↗

NASA NTRS1992

Experiments with recursive estimation in astronomical image processing

Recursive estimation concepts were applied to image enhancement problems since the 70's. However, very few applications in the particular area of astronomical image processing are known. These concepts were derived, for 2-dimensional images, from the well-known theory of Kalman filtering in one dimension. The historic reasons for application of these techniques to digital images are related to the images' scanned nature, in which the temporal output of a scanner device can be processed on-line by techniques borrowed directly from 1-dimensional recursive signal analysis. However, recursive estimation has particular properties that make it attractive even in modern days, when big computer memories make the full scanned image available to the processor at any given time. One particularly important aspect is the ability of recursive techniques to deal with non-stationary phenomena, that is, phenomena which have their statistical properties variable in time (or position in a 2-D image). Many image processing methods make underlying stationary assumptions either for the stochastic field being imaged, for the imaging system properties, or both. They will underperform, or even fail, when applied to images that deviate significantly from stationarity. Recursive methods, on the contrary, make it feasible to perform adaptive processing, that is, to process the image by a processor with properties tuned to the image's local statistical properties. Recursive estimation can be used to build estimates of images degraded by such phenomena as noise and blur. We show examples of recursive adaptive processing of astronomical images, using several local statistical properties to drive the adaptive processor, as average signal intensity, signal-to-noise and autocorrelation function. Software was developed under IRAF, and as such will be made available to interested users.

Busko, I.↗

NASA NTRS2000

Retinex Preprocessing for Improved Multi-Spectral Image Classification

The goal of multi-image classification is to identify and label "similar regions" within a scene. The ability to correctly classify a remotely sensed multi-image of a scene is affected by the ability of the classification process to adequately compensate for the effects of atmospheric variations and sensor anomalies. Better classification may be obtained if the multi-image is preprocessed before classification, so as to reduce the adverse effects of image formation. In this paper, we discuss the overall impact on multi-spectral image classification when the retinex image enhancement algorithm is used to preprocess multi-spectral images. The retinex is a multi-purpose image enhancement algorithm that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The retinex has been successfully applied to the enhancement of many different types of grayscale and color images. We show in this paper that retinex preprocessing improves the spatial structure of multi-spectral images and thus provides better within-class variations than would otherwise be obtained without the preprocessing. For a series of multi-spectral images obtained with diffuse and direct lighting, we show that without retinex preprocessing the class spectral signatures vary substantially with the lighting conditions. Whereas multi-dimensional clustering without preprocessing produced one-class homogeneous regions, the classification on the preprocessed images produced multi-class non-homogeneous regions. This lack of homogeneity is explained by the interaction between different agronomic treatments applied to the regions: the preprocessed images are closer to ground truth. The principle advantage that the retinex offers is that for different lighting conditions classifications derived from the retinex preprocessed images look remarkably "similar", and thus more consistent, whereas classifications derived from the original images, without preprocessing, are much less similar.

Thompson, B.↗