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The topics are presented in viewgraph form and include the following: features, applications, surface relief diffractive optics, optical data storage, waveguide lenses, diffractive lense imaging, phase grating synthesis, sub-wavelength structured surfaces, etc.
Digital enhancement of LANDSAT imagery was obtained by application of principal component analysis separately on each of the classes previously determined in a multispectral classification step. Each part of the image is thus enhanced whatever its spectral signature may be. A document was obtained which is a synthesis between a conventional image and an ordinary computerized classification. The interpreter can, at the same time, take into account not only the classification but also other features such as context and structure. An example is discussed with the help of geological interpretation.
A method of improving image fidelity by using observations at multiple frequencies is described. The power and possible limitations of the technique are discussed. Results of narrow frequency range observations with MERLIN and global VLBI are presented. The possible future impact of this technique is considered.
The Low Frequency Space Array (LFSA) is being designed to image the entire sky at extremely low radio frequencies with arcmin to subarcmin resolution. To accomplish this goal, data from LFSA will be continuously integrated for many months and then be used with aperture synthesis techniques to produce images. The three dimensional nature of LFSA and the effects of orbital geometry make LFSA a continuously evolving array which has an excellent synthesized point-response function. After transforming the data to produce an initial image, it is possible to remove low-level sidelobe responses remaining in the image and thereby produce a high dynamic-range image. Interference (both man-made and from solar-system objects) is a potential problem for LFSA, but appropriate data handling techniques are available which should eliminate any of its effects.
A program to investigate the properties of low surface brightness (LSB) galaxies involving surface photometry in U, B, V, R, I, and H-alpha, HI imaging with the Westerbork Synthesis Radio Telescope (WSRT) and the very large array (VLA) and spectrophotometry of H2 regions in LSB galaxies is underway. The goal is to verify the idea that LSB galaxies have low star formation rates because the local gas density falls below the critical density for star formation, and to study the stellar population and abundances in LSB galaxies. Such information should help understanding the evolutionary history of LSB galaxies. Some preliminary results are reported.
Soil moisture is a crucial variable for weather prediction because of its influence on evaporation. It is of critical importance for drought and flood monitoring and prediction and for public health applications. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented a new module in the NASA Land Information System (LIS) to assimilate observations from the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. SMOS Level 2 retrievals from the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument are assimilated into the Noah LSM within LIS via an Ensemble Kalman Filter. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Parallel runs with and without SMOS assimilation are performed with precipitation forcing from intentionally degraded observations, and then validated against a model run using the best available precipitation data, as well as against selected station observations. The goal is to demonstrate how SMOS data assimilation can improve modeled soil states in the absence of dense rain gauge and radar networks.
Soil moisture is a crucial variable for weather prediction because of its influence on evaporation. It is of critical importance for drought and flood monitoring and prediction and for public health applications. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented a new module in the NASA Land Information System (LIS) to assimilate observations from the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. SMOS Level 2 retrievals from the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument are assimilated into the Noah LSM within LIS via an Ensemble Kalman Filter. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Parallel runs with and without SMOS assimilation are performed with precipitation forcing from intentionally degraded observations, and then validated against a model run using the best available precipitation data, as well as against selected station observations. The goal is to demonstrate how SMOS data assimilation can improve modeled soil states in the absence of dense rain gauge and radar networks.
Soil moisture is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with soil moisture observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for soil moisture is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the Soil Moisture and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS soil moisture observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to soil moisture concentration in roughly the top 2.5 cm of soil. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen soil, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths.
Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency's (ESA's) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration's (NASA's) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from theArgo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS.We also identify the impact of the corrections for atmospheric effectsrecently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.
We are developing a system for synthesizing image sequences the simulate the facial motion of a speaker. To perform this synthesis, we are pursuing two major areas of effort. We are developing the necessary computer graphics technology to synthesize a realistic image sequence of a person speaking selected speech sequences. Next, we are developing a model that expresses the relation between spoken phonemes and face/mouth shape. A subject is video taped speaking an arbitrary text that contains expression of the full list of desired database phonemes. The subject is video taped from the front speaking normally, recording both audio and video detail simultaneously. Using the audio track, we identify the specific video frames on the tape relating to each spoken phoneme. From this range we digitize the video frame which represents the extreme of mouth motion/shape. Thus, we construct a database of images of face/mouth shape related to spoken phonemes. A selected audio speech sequence is recorded which is the basis for synthesizing a matching video sequence; the speaker need not be the same as used for constructing the database. The audio sequence is analyzed to determine the spoken phoneme sequence and the relative timing of the enunciation of those phonemes. Synthesizing an image sequence corresponding to the spoken phoneme sequence is accomplished using a graphics technique known as morphing. Image sequence keyframes necessary for this processing are based on the spoken phoneme sequence and timing. We have been successful in synthesizing the facial motion of a native English speaker for a small set of arbitrary speech segments. Our future work will focus on advancement of the face shape/phoneme model and independent control of facial features.
A method for recovery of a 3D model of a cloud-like structure that is in motion and deforming but approximately governed by magnetic field properties is described. The method allows recovery of the model from a single intensity image in which the structure's silhouette can be observed. The method exploits envelope theory and a magnetic field model. Given one intensity image and the segmented silhouette in the image, the method proceeds without human intervention to produce the 3D model. In addition to allowing 3D model synthesis, the method's capability to yield a very compact description offers further utility. Application of the method to several real-world images is demonstrated.
The following details recent efforts undertaken at the NASA Ames Unitary Plan Wind Tunnel to design and deploy an advanced, institutional, production-level data system for the classical Schlieren-shadowgraph technique. Motivation for the selection of individual system components is discussed along with a software methodology that combines image acquisition and processing into a production-level wind tunnel test measurement. In general terms, a production-level measurement refers to any data system that is seamlessly integrated into the primary wind tunnel data system, and whose data products are available real-time (e.g. force and moment, pressure, temperature data). The advantage of integrating a measurement in such a manner is an immediate increase in data product efficiency, productivity, reliability, and quality. Coupled with these benefits and leveraging recent advancements in high-speed imaging and image processing, automated, synchronized, time-resolved Schlieren-shadowgraph imaging for dynamic flow phenomena is now a reality. This makes possible the synthesis of dynamic off-body imaging with unsteady on-body measurements to produce a uniquely descriptive data product invaluable to the modern researcher.
A report discusses the continuing development of Windows Interface for Nominal Displacement Selection (WINDS), a computer program for automated analysis of images of the Sun and planets acquired by scientific instruments aboard spacecraft. WINDS is intended to afford capabilities for identification of features, measurement of displacements and velocities, analysis of terrain and of atmospheres, and synthesis of animation sequences of images of terrains and atmospheres from small sets of samples by use of velocity-based interpolation. A major element of WINDS will be a nonlinear correlator capable of tracking small features in complex image sequences. For dynamic image sequences, the correlator will enable compression of data by factors >100. In processing image data, WINDS will take account of such factors as texture in image data, rotation of features during measurement intervals, effects of viewing and solar illumination angles, and vertical structures of atmospheres. WINDS will also take account of positions, aiming directions, and fields of view of cameras to determine three-dimensional feature structures by use of triangulation and stereoscopic analysis techniques.
Norden E. Huang et al. had proposed and published the Hilbert-Huang Transform (HHT) concept correspondently in 1996, 1998. The HHT is a novel method for adaptive spectral analysis of non-linear and non-stationary signals. The HHT comprises two components: - the Huang Empirical Mode Decomposition (EMD), resulting in an adaptive data-derived basis of Intrinsic Mode functions (IMFs), and the Hilbert Spectral Analysis (HSA1) based on the Hilbert Transform for 1-dimension (1D) applied to the EMD IMF's outcome. Although paper describes the HHT concept in great depth, it does not contain all needed methodology to implement the HHT computer code. In 2004, Semion Kizhner and Karin Blank implemented the reference digital HHT real-time data processing system for 1D (HHT-DPS Version 1.4). The case for 2-Dimension (2D) (HHT2) proved to be difficult due to the computational complexity of EMD for 2D (EMD2) and absence of a suitable Hilbert Transform for 2D spectral analysis (HSA2). The real-time EMD2 and HSA2 comprise the real-time HHT2. Kizhner completed the real-time EMD2 and the HSA2 reference digital implementations respectively in 2013 & 2014. Still, the HHT2 outcome synthesis remains an active research area. This paper presents the initial concepts and preliminary results of HHT2-based synthesis and its application to processing of signals contaminated by Radio-Frequency Interference (RFI), as well as optical systems' fringe detection and mitigation at design stage. The Soil Moisture Active Passive (SMAP mission (SMAP) carries a radiometer instrument that measures Earth soil moisture at L1 frequency (1.4 GHz polarimetric - H, V, 3rd and 4th Stokes parameters). There is abundant RFI at L1 and because soil moisture is a strategic parameter, it is important to be able to recover the RFI-contaminated measurement samples (15% of telemetry). State-of-the-art only allows RFI detection and removes RFI-contaminated measurements. The HHT-based analysis and synthesis facilitates recovery of measurements contaminated by all kinds of RFI, including jamming [7-8]. The fringes are inherent in optical systems and multi-layer complex contour expensive coatings are employed to remove the unwanted fringes. HHT2-based analysis allows test image decomposition to analyze and detect fringes, and HHT2-based synthesis of useful image.
This paper presents a new radio image of the supernova remnant Puppis A obtained with the Molonglo Observatory synthesis telescope (MOST) and the Parkes telescope, and infrared images of Puppis A and its surrounding medium obtained with the IRAS, and compares the remnant morphologies at radio, infrared, optical, and X-ray wavelengths. The radio image, which includes large-scale emission, shows a clumpy shell of emission with moderate central brightness. The infrared observations have the advantage of offering a detailed look at the medium into which the remnant is expanding. From the IR data, evidence is found of the remnant's expansion into a strong density gradient (distinct from the more gentle gradient of the Galactic plane). A strong isolated knot of enhanced X-ray and radio emission is a region of enhanced infrared emission as well. Detailed correlative analysis of the infrared, radio, and X-ray emission shows that the IR emission correlates very well with the X-ray emission, confirming the idea that the IR emission arises from dust that is collisionally heated by the shocked gas.
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Solar microwave emission contains essential information for the study of the coronal magnetic structure of active regions and of thermal and nonthermal flare electrons. To exploit this potential requires BOTH imaging and spectroscopy with sufficient resolution to resolve spatial and spectral features. The VLA provides excellent solar imaging (when in the C and D configurations) but inadequate spectral coverage. The existing Owens Valley system has excellent spectral coverage but imaging that is adequate only for very simple sources. The Owens Valley system is currently undergoing an expansion, which when completed in October 1990 will provide a SOLAR-DEDICATED 5 antenna array (10 baselines). By using frequency-synthesis, this will provide a significant imaging capability in addition to its current spectral coverage.
A system is described which allows for the synthesis of a video sequence of a realistic-appearing talking human head. A phonic based approach is used to describe facial motion; image processing rather than physical modeling techniques are used to create video frames.