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All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Radiometric sensitivity comparisons of multispectral imaging systems

Multispectral imaging systems provide much of the basic data used by the land and ocean civilian remote-sensing community. There are numerous multispectral imaging systems which have been and are being developed. A common way to compare the radiometric performance of these systems is to examine their noise-equivalent change in reflectance, NE Delta-rho. The NE Delta-rho of a system is the reflectance difference that is equal to the noise in the recorded signal. A comparison is made of the noise equivalent change in reflectance of seven different multispectral imaging systems (AVHRR, AVIRIS, ETM, HIRIS, MODIS-N, SPOT-1, HRV, and TM) for a set of three atmospheric conditions (continental aerosol with 23-km visibility, continental aerosol with 5-km visibility, and a Rayleigh atmosphere), five values of ground reflectance (0.01, 0.10, 0.25, 0.50, and 1.00), a nadir viewing angle, and a solar zenith angle of 45 deg.

Lu, Nadine C.↗
NASA NTRS1990

Acousto-optic tunable filter field spectrometer for validation of airborne and spaceborne imaging spectrometers

A new concept for a field portable spectrometer designed to meet the needs of the remote sensing community is presented. This instrument uses acoustooptic tunable filters (AOTFs) as wavelength sorters, allowing the design of a rugged, compact, light-weight tool that provides broad spectral coverage, great versatility, and ease of utilization. The spectrometer provides continuous spectral coverage from 0.4 to 2.5 microns with two channels defined by detector technology, while a visible channel covering the 0.4 to 1.0 micron spectral range uses silicon PV photodiodes. The short-wavelength IR channel covers the 0.9 to 2.5 micron special range with thermoelectrically cooled lead sulfide PC detectors.

Rider, David M.↗
NASA NTRS1994

Beam scanning offset Cassegrain reflector antennas by subreflector movement

In 1987 a NASA panel recommended the creation of the Mission to Planet Earth. This mission was intended to apply to remote sensing experience of the space community to earth remote sensing to enhance the understanding of the climatological processes of our planet and to determine if, and to what extent, the hydrological cycle of Earth is being affected by human activity. One of the systems required for the mission was a wide scanning, high gain reflector antenna system for use in radiometric remote sensing from geostationary orbit. This work describes research conducted at Virginia Tech into techniques for beam scanning offset Cassegrain reflector antennas by subreflector translation and rotation. Background material relevant to beam scanning antenna systems and offset Cassegrain reflector antenna system is presented. A test case is developed based on the background material. The test case is beam scanned using two geometrical optics methods of determining the optimum subreflector position for the desired scanned beam direction. Physical optics far-field results are given for the beam scanned systems. The test case system is found to be capable of beam scanning over a range of 35 half-power beamwidths while maintaining a 90 percent beam efficiency or 50 half-power beamwidths while maintaining less than l dB of gain loss during scanning.

Lapean, James W., Jr.↗
NASA NTRS1994

VEG: An intelligent workbench for analysing spectral reflectance data

An Intelligent Workbench (VEG) was developed for the systematic study of remotely sensed optical data from vegetation. A goal of the remote sensing community is to infer the physical and biological properties of vegetation cover (e.g. cover type, hemispherical reflectance, ground cover, leaf area index, biomass, and photosynthetic capacity) using directional spectral data. VEG collects together, in a common format, techniques previously available from many different sources in a variety of formats. The decision as to when a particular technique should be applied is nonalgorithmic and requires expert knowledge. VEG has codified this expert knowledge into a rule-based decision component for determining which technique to use. VEG provides a comprehensive interface that makes applying the techniques simple and aids a researcher in developing and testing new techniques. VEG also provides a classification algorithm that can learn new classes of surface features. The learning system uses the database of historical cover types to learn class descriptions of one or more classes of cover types.

Harrison, P. Ann↗
NASA NTRS1994

Application of AI techniques to infer vegetation characteristics from directional reflectance(s)

Traditionally, the remote sensing community has relied totally on spectral knowledge to extract vegetation characteristics. However, there are other knowledge bases (KB's) that can be used to significantly improve the accuracy and robustness of inference techniques. Using AI (artificial intelligence) techniques a KB system (VEG) was developed that integrates input spectral measurements with diverse KB's. These KB's consist of data sets of directional reflectance measurements, knowledge from literature, and knowledge from experts which are combined into an intelligent and efficient system for making vegetation inferences. VEG accepts spectral data of an unknown target as input, determines the best techniques for inferring the desired vegetation characteristic(s), applies the techniques to the target data, and provides a rigorous estimate of the accuracy of the inference. VEG was developed to: infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; infer percent ground cover from any combination of nadir and/or off-nadir view angles; infer unknown view angle(s) from known view angle(s) (known as view angle extension); and discriminate between user defined vegetation classes using spectral and directional reflectance relationships developed from an automated learning algorithm. The errors for these techniques were generally very good ranging between 2 to 15% (proportional root mean square). The system is designed to aid scientists in developing, testing, and applying new inference techniques using directional reflectance data.

Kimes, D. S.↗
NASA NTRS1999

LANDSAT 7: Early on-Orbit Results

As this article was being submitted in mid-March, 1999, Landsat 7 had been cleared for an official launch date of April, 15, 1999, approximately 4 - 5 weeks prior to the Portland ASPRS conference. Although it is hoped that the presentation in Portland will be the first public status report on the in-orbit performance of the Landsat 7 spacecraft and the ETM+ instrument, it is impossible to discuss "early on-orbit performance" prior to launch. Therefore, we have chosen to summarize the overarching salient features of the Landsat 7 program, and we will point to some web sites where additional information about the program can be found (e.g., http://geo.arc.nasa.gov/sge/landsat/landsat. html). At this time, the Landsat Project Science Office is pleased to report that the performance of the ETM+ instrument appears to be very good. In addition to excellent instrument performance, a robust data acquisition plan has been developed with the goal of acquiring a seasonally-refreshed archive of global land observations at the EROS Data Center annually. A ground processing system is being implemented at EROS that will be capable of capturing, processing and archiving 250 Landsat scenes per day, and delivering 100 scene products to users each day. The cost of a systematically-processed Level 1 product will be less than $600, and there will be no copyright protection on the data. The net result is that the use of remote sensing data in our daily lives is expected to grow dramatically. This growth is expected to benefit all facets of the land remote sensing community.

Williams, D. L.↗
NASA NTRS1999

Landsat 7: An Early Look at On-Orbit Performance

As this article was being submitted in mid-March, 1999, Landsat 7 had been cleared for an official launch date of April 15, 1999, approximately 2 and 1/2 months prior to the 21st Canadian Symposium on Remote Sensing. Since it is impossible to discuss "early on-orbit performance" prior to the actual launch of the satellite, we have chosen to briefly summarize the major features of the Landsat 7 program. Additional information can be found at several web sites which will summarized at the end of this paper. At this time, the Landsat Project Science Office is pleased to report that the performance of the ETM+ instrument appears to be very good. In addition to excellent instrument performance, a robust data acquisition plan has been developed with the goal of acquiring and systematically refreshing a global archive of land observations at the EROS Data Center annually. A ground processing system is being implemented at EROS that will be capable of capturing, processing and archiving 250 Landsat scenes per day, and delivering 100 scene products to users daily. In addition, the cost of a systematically-processed Level 1 product will be less than $600, and there will be no copyright protection on the data. The net result is that the use of remote sensing data in our daily lives is expected to grow dramatically. This growth is expected to benefit all facets of the land remote sensing community.

Williams, D. L.↗
NASA NTRS1999

First Look at Landsat-7 Mission Performance: Technical and Operational Results to Date

A primary goal of the current Landsat-7 mission, launched on April 15, 1999, is to acquire and refresh on a seasonal basis, calibrated ata sets of multispectral digital imagery of the landmass of the Earth The Enhanced Thematic Mapper Plus (ETM+) imager flown on Landsat-7 provides ground spatial resolutions in the panchromatic, reflective and emissive bands of 15, 30 and 60 meters, respectively, for a nominal scene 183 km wide by 170 km long. This mission not only builds on the invaluable 27-year continuous archive of thematic images of the Earth provided by previous Landsat satellites, it also inaugurates a new era of robust data acquisition with an emphasis on global change science. The newly developed Long Term Acquisition Plan (LTAP) is being used to optimize the systematic collection of data from all parts of the globe, populating the U.S.-held archive at the USGS EROS Data Center (EDC) with over 90,000 Landsat scene per year . An additional 73,000 Images are expected to be acquired each year by several international ground stations, for a total downlink of Landsat7 data in excess of 100 terabytes per year. Nearly 20,000 scan of Landsat-7 ETM+ data have already been acquired in the first 100 days of the mission. Early results derived from assessments of the ETM+ instrument, the spacecraft, and the ground processing systems indicate that the image quality is outstanding, clearly the best ever provided by any Landsat mission. Sensor radiometric background stability after the first 100 days in orbit is approximately 0.1 percent. Stability of the Full Aperture Solar Calibrator is approximately 0.3 percent, and mid-scale per pixel noise is approximately 0.6 percent. A ground processing system has been implemented at EDC which is capable of capturing, processing and archiving 250 Landsat scenes 9 per day, and delivering 100 scene products to seems each day. The cost of a systematically-processed Level 1 product has been dropped dramatically to $600, end there is no longer any copyright protection an the data. The net result is that the use of Landsat ETM+ data is expected to grow dramatically, and this growth is expected to benefit all facets of the land remote sensing community.

Williams, Darrel L.↗
NASA NTRS2000

Wavelet and Fractal Analysis of Remotely Sensed Surface Temperature with Applications to Estimation of Surface Sensible Heat Flux Density

Wavelet and fractal analyses have been used successfully to analyze one-dimensional data sets such as time series of financial, physical, and biological parameters. These techniques have been applied to two-dimensional problems in some instances, including the analysis of remote sensing imagery. In this respect, these techniques have not been widely used by the remote sensing community, and their overall capabilities as analytical tools for use on satellite and aircraft data sets is not well known. Wavelet and fractal analyses have the potential to provide fresh insight into the characterization of surface properties such as temperature and emissivity distributions, and surface processes such as the heat and water vapor exchange between the surface and the lower atmosphere. In particular, the variation of sensible heat flux density as a function of the change In scale of surface properties Is difficult to estimate, but - in general - wavelets and fractals have proved useful in determining the way a parameter varies with changes in scale. We present the results of a limited study on the relationship between spatial variations in surface temperature distribution and sensible heat flux distribution as determined by separate wavelet and fractal analyses. We analyzed aircraft imagery obtained in the thermal infrared (IR) bands from the multispectral TIMS and hyperspectral MASTER airborne sensors. The thermal IR data allows us to estimate the surface kinetic temperature distribution for a number of sites in the Midwestern and Southwestern United States (viz., San Pedro River Basin, Arizona; El Reno, Oklahoma; Jornada, New Mexico). The ground spatial resolution of the aircraft data varied from 5 to 15 meters. All sites were instrumented with meteorological and hydrological equipment including surface layer flux measuring stations such as Bowen Ratio systems and sonic anemometers. The ground and aircraft data sets provided the inputs for the wavelet and fractal analyses, and the validation of the results.

Schieldge, John↗
NASA NTRS1986

Remote Sensing Spinoff

Delta Data Systems Inc., founded by ex-NASA engineers, used ELAS, a COSMIC-provided computer program for processing remotely sensed data as a starting point for its development of ATLAS. ATLAS is used to process satellite and aircraft data, to digitize soil topographic maps, and to generate land use maps. Among its applications are medical digital processing, food processing, and specialized services for the remote sensing community.

Source record↗
NASA NTRS2002

Sensor Web Architectural Concepts and Implementation Challenges - An Heuristic Approach

There is a significant interest in the Earth Science remote sensing community to increase the number of observations. The obvious reasons for such a push is to improve the temporal and surface coverage of measurements. However, there is little analysis available in terms of benefits, costs and optimized set of sensors needed to make these necessary observations. In reality, this is a complex problem that should be carefully studied and balanced over many boundaries. For example, the question of technology maturity versus users desire to obtain additional measurements is non congruent. This is further complicated by the limitations of the laws of physics and the economic conditions. With the advent of advance technology, it is anticipated that the cost of the spacecraft technology will become more affordable. However, the specialized detector subsystems, and the precision flying techniques may still require substantial innovation, development time and cost. Additionally, the space deployment scheme should also be given a careful attention because of a high expense. Nonetheless, it is important to carefully examine the science priorities and steer the development efforts that can commensurate with the tangible requirements. This paper outlines a possible set of architectural concepts, operational scenarios and potential benefits of one scheme versus another. It further makes some suggestions where one can draw some boundary conditions to incrementally solve this predicament.

Habib, Shahid↗
NASA NTRS2002

Sensor Web and Intelligent Sensors for Earth Science Applications

There is a significant interest in the Earth Science remote sensing community in substantially increasing the number of observations relative to the current frequency of collection. The obvious reason for such a push is to improve the temporal and surface coverage of measurements. However, there is little analysis available in terms of benefits, costs and optimized set of sensors needed to make these necessary observations. This is a complex problem that should be carefully studied and balanced over many boundaries. For example, the question of technology maturity versus users' desire for obtaining additional measurements is noncongruent. This is further complicated by the limitations of the laws of physics and the economic conditions. With the advent of advanced technology, it is anticipated that developments in spacecraft technology will enable advanced capabilities to become more affordable. However, specialized detector subsystems, and precision flying techniques may still require substantial innovation, development time and cost. Additionally, the space deployment scheme should also be given careful attention because of the high associated expense. Nonetheless, it is important to carefully examine the science priorities and steer the development efforts that can commensurate with the tangible requirements. This presentation will focus on a possible set of architectural concepts beneficial for future Earth science studies and research its and potential benefits.

Habib, Shahid↗
NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental building block in numerous image processing applications. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute the coordinates of a set of cluster centers in d-space, such that those centers minimize the mean squared distance from each data point to its nearest center. This clustering algorithm is similar to another well-known clustering method, called k-means. One significant feature of ISOCLUS over k-means is that the actual number of clusters reported might be fewer or more than the number supplied as part of the input. The algorithm uses different heuristics to determine whether to merge lor split clusters. As ISOCLUS can run very slowly, particularly on large data sets, there has been a growing .interest in the remote sensing community in computing it efficiently. We have developed a faster implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm of Kanungo, et al. They showed that, by using a kd-tree data structure for storing the data, it is possible to reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm, and we show that it is possible to achieve essentially the same results as ISOCLUS on large data sets, but with significantly lower running times. This adaptation involves computing a number of cluster statistics that are needed for ISOCLUS but not for k-means. Both the k-means and ISOCLUS algorithms are based on iterative schemes, in which nearest neighbors are calculated until some convergence criterion is satisfied. Each iteration requires that the nearest center for each data point be computed. Naively, this requires O(kn) time, where k denotes the current number of centers. Traditional techniques for accelerating nearest neighbor searching involve storing the k centers in a data structure. However, because of the iterative nature of the algorithm, this data structure would need to be rebuilt with each new iteration. Our approach is to store the data points in a kd-tree data structure. The assignment of points to nearest neighbors is carried out by a filtering process, which successively eliminates centers that can not possibly be the nearest neighbor for a given region of space. This algorithm is significantly faster, because large groups of data points can be assigned to their nearest center in a single operation. Preliminary results on a number of real Landsat datasets show that our revised ISOCLUS-like scheme runs about twice as fast.

Memarsadeghi, Nargess↗
NASA NTRS2004

Virtual Sensors: Using Data Mining Techniques to Efficiently Estimate Remote Sensing Spectra

Various instruments are used to create images of the Earth and other objects in the universe in a diverse set of wavelength bands with the aim of understanding natural phenomena. These instruments are sometimes built in a phased approach, with some measurement capabilities being added in later phases. In other cases, there may not be a planned increase in measurement capability, but technology may mature to the point that it offers new measurement capabilities that were not available before. In still other cases, detailed spectral measurements may be too costly to perform on a large sample. Thus, lower resolution instruments with lower associated cost may be used to take the majority of measurements. Higher resolution instruments, with a higher associated cost may be used to take only a small fraction of the measurements in a given area. Many applied science questions that are relevant to the remote sensing community need to be addressed by analyzing enormous amounts of data that were generated from instruments with disparate measurement capability. This paper addresses this problem by demonstrating methods to produce high accuracy estimates of spectra with an associated measure of uncertainty from data that is perhaps nonlinearly correlated with the spectra. In particular, we demonstrate multi-layer perceptrons (MLPs), Support Vector Machines (SVMs) with Radial Basis Function (RBF) kernels, and SVMs with Mixture Density Mercer Kernels (MDMK). We call this type of an estimator a Virtual Sensor because it predicts, with a measure of uncertainty, unmeasured spectral phenomena.

Srivastava, Ashok N.↗
NASA NTRS2004

Formation Control for the Maxim Mission.

Over the next twenty years, a wave of change is occurring in the spacebased scientific remote sensing community. While the fundamental limits in the spatial and angular resolution achievable in spacecraft have been reached, based on today's technology, an expansive new technology base has appeared over the past decade in the area of Distributed Space Systems (DSS). A key subset of the DSS technology area is that which covers precision formation flying of space vehicles. Through precision formation flying, the baselines, previously defined by the largest monolithic structure which could fit in the largest launch vehicle fairing, are now virtually unlimited. Several missions including the Micro-Arcsecond X-ray Imaging Mission (MAXIM), and the Stellar Imager will drive the formation flying challenges to achieve unprecedented baselines for high resolution, extended-scene, interferometry in the ultraviolet and X-ray regimes. This paper focuses on establishing the feasibility for the formation control of the MAXIM mission. The Stellar Imager mission requirements are on the same order of those for MAXIM. This paper specifically addresses: (1) high-level science requirements for these missions and how they evolve into engineering requirements; (2) the formation control architecture devised for such missions; (3) the design of the formation control laws to maintain very high precision relative positions; and (4) the levels of fuel usage required in the duration of these missions. Specific preliminary results are presented for two spacecraft within the MAXIM mission.

Luquette, Richard J.↗
NASA NTRS2004

Space-Based Sensor Web for Earth Science Applications: An Integrated Architecture for Providing Societal Benefits

There is a significant interest in the Earth Science research and user remote sensing community to substantially increase the number of useful observations relative to the current frequency of collection. The obvious reason for such a push is to improve the temporal, spectral, and spatial coverage of the area(s) under investigation. However, there is little analysis available in terms of the benefits, costs and the optimal set of sensors needed to make the necessary observations. Classic observing system solutions may no longer be applicable because of their point design philosophy. Instead, a new intelligent data collection system paradigm employing both reactive and proactive measurement strategies with adaptability to the dynamics of the phenomena should be developed. This is a complex problem that should be carefully studied and balanced across various boundaries including: science, modeling, applications, and technology. Modeling plays a crucial role in making useful predictions about naturally occurring or human-induced phenomena In particular, modeling can serve to mitigate the potentially deleterious impacts a phenomenon may have on human life, property, and the economy. This is especially significant when one is interested in learning about the dynamics of, for example, the spread of forest fires, regional to large-scale air quality issues, the spread of the harmful invasive species, or the atmospheric transport of volcanic plumes and ash. This paper identifies and examines these challenging issues and presents architectural alternatives for an integrated sensor web to provide observing scenarios driving the requisite dynamic spatial, spectral, and temporal characteristics to address these key application areas. A special emphasis is placed on the observing systems and its operational aspects in serving the multiple users and stakeholders in providing societal benefits. We also address how such systems will take advantage of technological advancement in small spacecraft and emerging information technologies, and how sensor web options may be realized and made affordable. Specialized detector subsystems and precision flying techniques may still require substantial innovation, development time and cost: we have presented the considerations for these issues. Finally, data and information gathering and compression techniques are also briefly described.

Habib, Shahid↗
NASA NTRS2005

Martian Radiative Transfer Modeling Using the Optimal Spectral Sampling Method

The large volume of existing and planned infrared observations of Mars have prompted the development of a new martian radiative transfer model that could be used in the retrievals of atmospheric and surface properties. The model is based on the Optimal Spectral Sampling (OSS) method [1]. The method is a fast and accurate monochromatic technique applicable to a wide range of remote sensing platforms (from microwave to UV) and was originally developed for the real-time processing of infrared and microwave data acquired by instruments aboard the satellites forming part of the next-generation global weather satellite system NPOESS (National Polarorbiting Operational Satellite System) [2]. As part of our on-going research related to the radiative properties of the martian polar caps, we have begun the development of a martian OSS model with the goal of using it to perform self-consistent atmospheric corrections necessary to retrieve caps emissivity from the Thermal Emission Spectrometer (TES) spectra. While the caps will provide the initial focus area for applying the new model, it is hoped that the model will be of interest to the wider Mars remote sensing community.

Eluszkiewicz, J.↗
NASA NTRS2005

Improvements in Virtual Sensors: Using Spatial Information to Estimate Remote Sensing Spectra

Various instruments are used to create images of the Earth and other objects in the universe in a diverse set of wavelength bands with the aim of understanding natural phenomena. Sometimes these instruments are built in a phased approach, with additional measurement capabilities added in later phases. In other cases, technology may mature to the point that the instrument offers new measurement capabilities that were not planned in the original design of the instrument. In still other cases, high resolution spectral measurements may be too costly to perform on a large sample and therefore lower resolution spectral instruments are used to take the majority of measurements. Many applied science questions that are relevant to the earth science remote sensing community require analysis of enormous amounts of data that were generated by instruments with disparate measurement capabilities. In past work [1], we addressed this problem using Virtual Sensors: a method that uses models trained on spectrally rich (high spectral resolution) data to "fill in" unmeasured spectral channels in spectrally poor (low spectral resolution) data. We demonstrated this method by using models trained on the high spectral resolution Terra MODIS instrument to estimate what the equivalent of the MODIS 1.6 micron channel would be for the NOAA AVHRR2 instrument. The scientific motivation for the simulation of the 1.6 micron channel is to improve the ability of the AVHRR2 sensor to detect clouds over snow and ice. This work contains preliminary experiments demonstrating that the use of spatial information can improve our ability to estimate these spectra.

Oza, Nikunj C.↗