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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Statistical theory and methodology for remote sensing data analysis with special emphasis on LACIE

Crop proportion estimators for determining crop acreage through the use of remote sensing were evaluated. Several studies of these estimators were conducted, including an empirical comparison of the different estimators (using actual data) and an empirical study of the sensitivity (robustness) of the class of mixture estimators. The effect of missing data upon crop classification procedures is discussed in detail including a simulation of the missing data effect. The final problem addressed is that of taking yield data (bushels per acre) gathered at several yield stations and extrapolating these values over some specified large region. Computer programs developed in support of some of these activities are described.

Odell, P. L.↗
NASA NTRS1980

CCD data processor for maximum likelihood feature classification

The paper describes an advanced technology development which utilizes a high speed analog/binary CCD correlator to perform the matrix multiplications necessary to implement onboard feature classification. The matrix manipulation module uses the maximum likelihood classification algorithm assuming a Gaussian probability density function. The module will process 16 element multispectral vectors at rates in excess of 500 thousand multispectral vector elements per second. System design considerations for the optimum use of this module are discussed, test results from initial device fabrication runs are presented, and the performance in typical processing applications is described

Benz, H. F.↗
NASA NTRS1980

Crop classification with a Landsat/radar sensor combination

A combined Landsat/radar approach to classification of remotely sensed data, with emphasis on crops, was undertaken. Radar data were obtained by microwave radar spectrometers over fields near Eudora, Kansas and Landsat image data were obtained for the same test site. After Landsat digital images were registered and test-cells extracted, a comparable set of radar image pixels were simulated to match the Landsat pixels. The combined data set is then used for classification, and the results are examined with the best combination of sensor variables identified. Finally, the usefulness of radar in a simulated cloud-cover situation is demonstrated. The major conclusion derived from this study is that the combination of radar/optical sensors is superior to either one alone.

Li, R. Y.↗
NASA NTRS1979

Sampling for area estimation: A comparison of full-frame sampling with the sample segment approach

The effect of sampling on the accuracy (precision and bias) of crop area estimates made from classifications of LANDSAT MSS data was investigated. Full-frame classifications of wheat and non-wheat for eighty counties in Kansas were repetitively sampled to simulate alternative sampling plants. Four sampling schemes involving different numbers of samples and different size sampling units were evaluated. The precision of the wheat area estimates increased as the segment size decreased and the number of segments was increased. Although the average bias associated with the various sampling schemes was not significantly different, the maximum absolute bias was directly related to sampling unit size.

Hixson, M. M.↗
NASA NTRS1982

Ground Truth Sampling and LANDSAT Accuracy Assessment

It is noted that the key factor in any accuracy assessment of remote sensing data is the method used for determining the ground truth, independent of the remote sensing data itself. The sampling and accuracy procedures developed for nuclear power plant siting study are described. The purpose of the sampling procedure was to provide data for developing supervised classifications for two study sites and for assessing the accuracy of that and the other procedures used. The purpose of the accuracy assessment was to allow the comparison of the cost and accuracy of various classification procedures as applied to various data types.

Robinson, J. W.↗
NASA NTRS1984

Simple descriptor for contextual classification of hyperdimensional remotely sensed spectral data

An extended CIE transformation was employed to deal with data reduction problems of hyperdimensional spectral data for NASA airborne and Shuttle imaging spectrometers. A simple descriptor was found to be very effective in analyzing spectral data covers from 0.4 to 2.4 microns. Results reveal contextual properties of minerals, vegetation, and crops. It provides a means to monitor seasonal growth variations for crops. It can also serve as pseudocolor indexing for imaging spectrometer imagery extended beyond the visible range.

Chiou, W. C.↗
NASA NTRS1984

Dynamic descriptors for contextual classification of remotely sensed hyperspectral image data analysis

The extended CIE transformation procedure of Chiou (1984) is applied to five sets of remotely sensed 0.4-2.5-micron spectrometric data on field crops including winter and spring wheat, corn, and soybeans) obtained at the Purdue University Laboratory for Applications of - Remote Sensing during 1977-1980 using the techniques described by Hinzman (1981). The results are presented in tables and chromaticity diagrams and it is found that each crop has an identifiable time-variant spectral characteristic permitting determination of seasonal growth patterns. The applicability of the method to data from airborne and spaceborne imaging spectrometers is indicated.

Chiou, W. C., Sr.↗
NASA NTRS1984

Comparison of classification schemes for MSS and TM data

The launch of the Landsat-4 satellite in July 1982 provided the first full coverage from space of the 0.4-12 micron spectrum of the earth scene. In addition to the green, red, and near IR bands of the MSS, the TM provides a band in the blue, two in the middle IR, and one thermal IR. The paper describes spectral class analysis of coincident MSS and TM data to evaluate the contribution of the additional TM bands. In addition, various classifiers are available which were applied to the TM data. In the spectral class analysis, twice the number of separable classes was found in the TM data compared to the MSS data.

Anuta, P. E.↗
NASA NTRS1990

Classification Of Radar Scatterers Via Polarimetric Data

Scattering mechanisms identified via polarization signatures. Algorithm automatically classifies radar-backscattering mechanisms in images produced by synthetic-aperture-radar polarimeter. Uses full polarimetric data from each picture element. These data generally expressed in terms of complex 2 by 2 scattering matrix equivalent to three independent amplitudes and three independent phases representing relationships between horizontally- and vertically-polarized components of transmitted and backscattered signals.

Van Zyl, Jakob J.↗
NASA NTRS1993

First results of the seven-color asteroid survey

The new Seven-Color infrared filter system (SCAS), designed specifically to capture the essential mineralogical information present in asteroid spectra, is composed of seven broad-band filters which allow for IR observations of objects as faint as 17th magnitude. The first test of the SCAS system occurred in Jul. 1992. In four nights at the IRTF on Mauna Kea, Hawaii, over 67 objects were observed. Five of the observations were to test the new system for accuracy relative to previous observations with the high-resolution 52 Color Infrared Survey and with the Eight-Color Asteroid Survey (ECAS). In three cases, the match with previous data was good. In two cases, the match to previous observations was not as good. In addition, sixty S-Type asteroids were measured with the SCAS system. Forty of those asteroids were also observed with the ECAS system. Among the new observations is infrared data of 371 Bohemia, a main belt asteroid which was classified 'QSV' according to its UBV colors in the taxonomic system of D.J. Tholen. There are no corresponding ECAS data for 371. Q-type asteroids are of special interest as they are proposed to be the elusive parent bodies of the ordinary chondrite meteorites. Most Q-types are Earth-crossing asteroids and have not yet been observed in the infrared (except, perhaps, 371). Positive identification of a large main belt Q-type would be of major importance in the scheme of the geological structure of the asteroid belt. Without visible wavelength data, however, the classification of 371 Bohemia remains ambiguous. An attempt to conjoin Bohemia SCAS data with ECAS data of both a typical Q-Type asteroid and an average S-Type asteroid is shown. This figure thus illustrates the importance of visible wavelength data to the SCAS system. In other words, without ECAS data of 371 Bohemia we cannot use its spectral characteristics to identify it as a possible parent body of ordinary chondrite meteorites.

Clark, Beth E.↗
NASA NTRS2004

Autonomous Vegetation Cover Scene Classification of EO-1 Hyperion Hyperspectral Data

The Autonomous Sciencecraft Experiment (ASE) is a JPL-led, New Millennium Program mission containing new technology in the form of software to be flown on the Earth Observer-1 (EO-1) satellite in early 2004. This new technology will facilitate an artificially intelligent machine with autonomous science-driven capabilities. Among the ASE flight software is a set of onboard science algorithms designed for autonomous data processing, primarily based on change detection from observation to observation. Using the output from these algorithms, ASE has the ability to autonomously modify the EO-1 observation plan, retargeting itself for a more in-depth observation of a scientific event in progress. Furthermore, intelligent and selective information down-linking will maximize return of the most valuable scientific data. Among the algorithms developed for use on ASE is a Lava-Vegetation (L-V) detection algorithm. This algorithm can effectively identify the initial location and extent of lava and vegetation coverage based on spectral shape. Comparison of several different observations, all classified via this algorithm, can make change detection possible.

Lee, R. J.↗
NASA NTRS2011

Canopy Spectral Invariants: Application to Classification of Forest Types from Hyperspectral Data - Part 2

Many studies have been conducted to demonstrate the ability of hyperspectral data to discriminate plant dominant species. Most of them have employed the use of empirically based techniques, which are site specific, requires some initial training based on characteristics of known leaf and/or canopy spectra and therefore may not be extendable to operational use or adapted to changing or unknown land cover. In this paper we propose a physically based approach for separation of dominant forest type using hyperspectral data. The radiative transfer theory of canopy spectral invariants underlies the approach, which facilitates parameterization of the canopy reflectance in terms of the leaf spectral scattering and two spectrally invariant and structurally varying variables - recollision and directional escape probabilities. The methodology is based on the idea of retrieving spectrally invariant parameters from hyperspectral data first, and then relating their values to structural characteristics of three-dimensional canopy structure. Theoretical and empirical analyses of ground and airborne data acquired by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over two sites in New England, USA, suggest that the canopy spectral invariants convey information about canopy structure at both the macro- and micro-scales. The total escape probability (one minus recollision probability) varies as a power function with the exponent related to the number of nested hierarchical levels present in the pixel. Its base is a geometrical mean of the local total escape probabilities and accounts for the cumulative effect of canopy structure over a wide range of scales. The ratio of the directional to the total escape probability becomes independent of the number of hierarchical levels and is a function of the canopy structure at the macro-scale such as tree spatial distribution, crown shape and size, within-crown foliage density and ground cover. These properties allow for the natural separation of dominant forest classes based on the location of points on the total escape probability vs the ratio log-log plane.

Schull, M. A.↗