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The earth resources study for California is reported. The objectives, user needs, water management, data acquisition, data processing, and atmospheric effects in image transfer are discussed.
LANDSAT imagery and NASA high altitude color infrared (CIR) photography were used to select suitable sites for sanitary landfill in Volusia County, Florida and to develop techniques for preventing sand deposits in the Clearwater inlet. Activities described include the acquisition of imagery, its analysis by the IMAGE 100 system, conventional photointerpretation, evaluation of existing data sources (vegetation, soil, and ground water maps), site investigations for ground truth, and preparation of displays for reports.
Major accomplishments in an effort to encourage investigation and technology transfer for practical applications of remote sensing to solve Earth resources problems in Vermont include: (1) acquisition, installation, and operation of the ORSER digital processing system on the University's IBM 3031 computer; (2) acquisition and operation of printing and CRT computer terminals for remote access to computer facilities for analysis of remotely sensed digital tape; (3) acquisition and operation of optical interpretation and image transfer devices for use with all types of aerial photography; (4) development of audio visual and other training materials for use in presentations, workshops, and short courses to enhance technology transfer; and (5) cooperation government agencies in demonstration projects to show the feasibility of using remote sensing technology.
Satellite-borne Synthetic Aperture Radars (SAR) sense areas of several thousand square kilometers in seconds and transmit phase history signal data several tens of megabits per second. The Shuttle Imaging Radar-B (SIR-B) has a variable swath of 20 to 50 km and acquired data over 100 kms along track in about 13 seconds. With the simplification of separability of the reference function, the processing still requires considerable resources; high speed I/O, large memory and fast computation. Processing systems with regular hardware take hours to process one Seasat image and about one hour for a SIR-B image. Bringing this processing time closer to acquisition times requires an end-to-end system solution. For the purpose of demonstration, software was implemented on the present Massively Parallel Processor (MPP) configuration for processing Seasat and SIR-B data. The software takes advantage of the high processing speed offered by the MPP, the large Staging Buffer, and the high speed I/O between the MPP array unit and the Staging Buffer. It was found that with unoptimized Parallel Pascal code, the processing time on the MPP for a 4096 x 4096 sample subset of signal data ranges between 18 and 30.2 seconds depending on options.
The goal of the basic research portion of the extravehicular activity (EVA) glove research program is to gain a greater understanding of the kinematics of the hand, the characteristics of the pressurized EVA glove, and the interaction of the two. Examination of the literature showed that there existed no acceptable, non-invasive method of obtaining accurate biomechanical data on the hand. For this reason a project was initiated to develop magnetic resonance imaging as a tool for biomechanical data acquisition and visualization. Literature reviews also revealed a lack of practical modeling methods for fabric structures, so a basic science research program was also initiated in this area.
In the 1970s NASA and the Department of Agriculture attempted to use the new Landsat MSS system for agricultural purposes. The program had relatively little success. With the advent of differential GPS, yield monitors on harvest equipment and higher spatial resolution remote sensing systems it seemed likely the situation should be reexamined. Therefore, a campaign of data acquisition involving remote sensing and other modalities with dependent research was assembled and funded by the Space Grant Consortia in Alabama and Georgia. The design of the remote sensing data acquisition was driven by the biology and physics of the crop system and limited by the available sensor platforms. Major parameters included crop stage, spatial resolution, seasonal and daily weather conditions, and which portion of the EM spectrum would actually capture the most discriminating information. Joint visible and Near IR with Thermal IR would permit use of existing indices, such as greenness, as well as phenomena driven by the plant' s evapotranspiration. Spatial resolution in the 2-5 meter range was chosen, avoiding many complexities caused by aliasing crop row spacing at, higher resolutions yet finer than the harvester's tightest recording rate. This dictates use of an airborne system. Use of an airborne system also makes scheduling around weather much simpler than use of satellite data. Active video calibration was recognized as essential if quantitative measures were ever to be obtained or reproduced. The system would also have to have onboard geoOF1 Based on these elements 3 data acquisitions have been flown. Seven flight lines were flown twice in 1998 and 16 lines flown in 1999. Total raw data is several GBytes. All of the data has now been geometrically corrected and some preliminary analysis accomplished. The thermal bands have an extremely high correlation with yield. For one@test case with corn, correlation in excess of 0.86 was obtained from a data acquisition two months prior to harvest! Soil images show significant within field variation in clay, soil brightness and emissivity. Light wind has been found to effect the reflectance and temperature of broad leaf crops, including soybeans, cotton and peanuts. Clearly, this work has already demonstrated some very important results. With continued development of the remote sensing technology there is good reason to believe this research will soon be able to help the individual farmer.
A new, molecular Rayleigh-scattering-based flow diagnostic developed at the NASA Glenn Research Center has been used for the first time to measure the power spectrum of both gas density and radial velocity components in the plumes of high-speed jets. The objective of the work is to develop an unseeded, nonintrusive dynamic measurement technique for studying turbulent flows in NASA test facilities. This technique provides aerothermodynamic data not previously obtainable. It is particularly important for supersonic flows, where hot wire and pitot probes are difficult to use and disturb the flow under study. The effort is part of the nonintrusive instrumentation development program supporting propulsion research at the NASA Glenn Research Center. In particular, this work is measuring fluctuations in flow velocity, density, and temperature for jet noise studies. These data are valuable to researchers studying the correlation of flow fluctuations with far-field noise. One of the main objectives in jet noise research is to identify noise sources in the jet and to determine their contribution to noise generation. The technique is based on analyzing light scattered from molecules within the jet using a Fabry-Perot interferometer operating in a static imaging mode. The PC-based data acquisition system can simultaneously sample velocity and density data at rates to about 100 kHz and can handle up to 10 million data records. We used this system to interrogate three different jet nozzle designs in a Glenn free-jet facility. Each nozzle had a 25.4-mm exit diameter. One was convergent, used for subsonic flow measurements and to produce a screeching underexpanded jet with a fully expanded Mach number of 1.42. The other nozzles (Mach 1.4 and 1.8) were convergent-divergent types. The radial component of velocity and gas density were simultaneously measured in this work.
Autonomous UAV transmission line inspection served as a reference mission for NASA demonstration of UAV deployment for economic benefit; this paper reports corona sensing advances in development of that reference mission. Unmanned aerial vehicles can serve as a platform for autonomous sensing and location of high voltage coronal discharge. Simple processing of commercial corona camera imagery can automate discharge localization and documentation. Inexpensive ultraviolet point sensors can sense discharge when carried close to the defect with a UAV. Augmented with a parabolic mirror, point sensor range can be increased to a safe inspection standoff distance, at the cost of a narrowed field of view. Results from a test flight of an augmented UV sensor are described. The imaging approach is superior in sensitivity and acquisition time, while the point sensor approach has superior size, weight, cost and durability advantages.
An approach to remote sensing that meets future mission requirements was investigated. The deterministic acquisition of data and the rapid correction of data for radiometric effects and image distortions are the most critical limitations of remote sensing. The following topics are discussed: onboard image correction systems, GCP navigation system simulation, GCP analysis, and image correction analysis measurement.
Understanding how neurons interact across the brain to control animal behaviors is one of the central goals in neuroscience. Recent developments in fluorescent microscopy and genetically-encoded calcium indicators led to the establishment of whole-brain imaging methods in zebrafish, which record neural activity across a brain-wide volume with single-cell resolution. Pioneering studies of whole-brain imaging used custom light-sheet microscopes, and their operation relied on commercially developed and maintained software not available globally. Hence it has been challenging to disseminate and develop the technology in the research community. Here, we present PyZebrascope, an open-source Python platform designed for neural activity imaging in zebrafish using light-sheet microscopy. PyZebrascope has intuitive user interfaces and supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its camera module can handle image data throughput of up to 800 MB/s from camera acquisition to file writing while maintaining stable CPU and memory usage. Its modular architecture allows the inclusion of advanced algorithms for microscope control and image processing. As a proof of concept, we implemented a novel automatic algorithm for maximizing the image resolution in the brain by precisely aligning the excitation beams to the image focal plane. PyZebrascope enables whole-brain neural activity imaging in fish behaving in a virtual reality environment. Thus, PyZebrascope will help disseminate and develop light-sheet microscopy techniques in the neuroscience community and advance our understanding of whole-brain neural dynamics during animal behaviors.
The Process Thresholds Working Group concerned itself with whether a geomorphic process to be monitored on satellite imagery must be global, regional, or local in its effect on the landscape. It was pointed out that major changes in types and magnitudes of processes operating in an area are needed to be detectable on a global scale. It was concluded from a review of geomorphic studies which used satellite images that they do record change in landscape over time (on a time-lapse basis) as a result of one or more processes. In fact, this may be one of the most important attributes of space imagery, in that one can document land form changes in the form of a permanent historical record. The group also discussed the important subject of the acquisition of basic data sets by different satellite imaging systems. Geomorphologists already have available one near-global basis data set resulting from the early LANDSAT program, especially images acquired by LANDSATs 1 and 2. Such historic basic data sets can serve as a benchmark for comparison with landscape changes that take place in the future. They can also serve as a benchmark for comparison with landscape changes that have occurred in the past (as recorded) by images, photography and maps.
A small, portable data-acquisition and display system with a unique coprocessor was designed and built to collect data from infrared array detectors for astronomical and laboratory use. The system consists of a microcomputer board, two 12-bit analog-to-digital data-acquisition boards, a color-graphics driver board, and two coprocessor boards. The coprocessors are arithmetic logic units designed to achieve the high computation rates necessary for real-time infrared background cancellation. The system can collect data from infrared detectors of 1 to 256 pixel elements with a computation rate of 768 kHz. The software for the system was designed to collect data in the system's background, while performing other tasks in the system's foreground. The data for the detector arrays is stored in block fashion to facilitate variable image size.
Two schemes for processing signals obtained from the Doppler global velocimeter are described. The analog approach is a simple, real-time method for obtaining an RS-170 video signal containing the normalized intensity image. Pseudocolors are added using a monochromatic frame grabber producing a standard NTSC video signal that can be monitored and/or recorded. The digital approach is more complicated, but maintains the full resolution of the acquisition cameras with the ability to correct the signal image for pixel sensitivity variations and to remove background light. Prototype circuits for each scheme are described, and example results from the investigation of the vortical flow field above a 75-deg delta wing are presented.
As part of the 1986 Long-Range Plan for the National Library of Medicine (NLM), the Planning Panel on Medical Education wrote that NLM should '...thoroughly and systematically investigate the technical requirements for and feasibility of instituting a biomedical images library.' The panel noted the increasing use of images in clinical practice and biomedical research. An image library would complement NLM's existing bibliographic and factual database services and would ideally be available through the same computer networks as are these current NLM services. Early in 1989, NLM's Board of Regents convened an ad hoc planning panel to explore possible roles for the NLM in the area of electronic image libraries. In its report to the Board of Regents, the NLM Planning Panel on Electronic Image Libraries recommended that 'NLM should undertake a first project building a digital image library of volumetric data representing a complete, normal adult male and female. This Visible Human Project will include digitized photographic images for cryosectioning, digital images derived from computerized tomography, and digital magnetic resonance images of cadavers.' The technologies needed to support digital high resolution image libraries, including rapid development; and that NLM encourage investigator-initiated research into methods for representing and linking spatial and textual information, structural informatics. The first part of the Visible Human Project is the acquisition of cross-sectional CT and MRI digital images and cross-sectional cryosectional photographic images of a representative male and female cadaver at an average of one millimeter intervals. The corresponding cross-sections in each of the three modalities are to be registerable with one another.
Two schemes for processing signals obtained from the Doppler global velocimeter are described. The analog approach is a simple, real time method for obtaining an RS-170 video signal containing the normalized intensity image. Pseudo colors are added using a monochromatic frame grabber producing a standard NTSC video signal that can be monitored and/or recorded. The digital approach is more complicated, but maintains the full resolution of the acquisition cameras with the capabilities to correct the signal image for pixel sensitivity variations and to remove of background light. Prototype circuits for each scheme are described and example results from the investigation of the vortical flow field above a 75-degree delta wing presented.
We describe two interim results from an ongoing effort to automate the acquisition, analysis, archiving, and distribution of satellite earth science data. Both results are applications of Artificial Intelligence planning research to the automatic generation of processing steps for image analysis tasks. First, we have constructed a linear conditional planner (CPed), used to generate conditional processing plans. Second, we have extended an existing hierarchical planning system to make use of durations, resources, and deadlines, thus supporting the automatic generation of processing steps in time and resource-constrained environments.
Digital image correlation (DIC) is an established test technique in several fields including quasi-static displacement measurements. Recently there has been growing interest in using DIC to measure structural dynamic response and even extract modal parameters from that information. Although high-speed cameras have become more ubiquitous, there are no commercial end-to-end packages for modal analysis based on image data, particularly when combined with traditional data acquisition systems. As such, the practitioner is left to develop several key data processing capabilities, hardware interface equipment, and testing practices themselves. This work highlights several practical aspects that have been encountered while establishing DIC as a viable modal testing capability in a laboratory environment.
Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.