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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

Apollo 15 coarse fines (4-10 mm): Sample classification, description and inventory

A particle by particle binocular microscopic examination of all of the Apollo 15 4-10 mm fines samples is reported. These particles are classified according to their macroscopic lithologic features in order to provide a basis for sample allocations and future study. The relatively large size of these particles renders them too vaulable to permit treatment along with the other bulk fines, yet they are too small (and numerous) to practically receive full individual descriptive treatment as given the larger rock samples. This examination, classification and description of subgroups represents a compromise treatment. In most cases and for many types of investigation the individual particles should be large enough to permit the application of more than one type of analysis.

Powell, B. N.↗
NASA NTRS1976

Linear dimensionality of Landsat agricultural data with implications for classification

A model for the Landsat multispectral scanner data, representing a generalization of the commonly used Gaussian model, has been formulated and analyzed. The model hypothesizes that the data for different crop types essentially lie on distinct hyperplanes in the feature space. Tests of this model reveal that: (1) the agricultural data from any single acquisition (i.e., four-channel) of Landsat are essentially two dimensional, regardless of the crop type; and (2) the data from different sites and different stages of crop development all lie on planes which are parallel. These findings have significant implications for data display, classification, feature extraction, and signature extension.

Wheeler, S. G.↗
NASA NTRS1976

Selecting class weights to minimize classification bias in acreage estimation

Preliminary results of experiments being performed to select optimal class weights for use with the maximum likelihood classifier in acreage estimation using remote sensor imagery are presented. These weights will be optimal in the sense that the bias will be minimized in the proportion estimate obtained from the classification results by sample counting. The procedure was tested using Landsat MSS data from an 8 by 9.6 km area of ground truth in Finney County, Kansas.

Belcher, W. M.↗
NASA NTRS1976

Number of signatures necessary for accurate classification

This paper presents a procedure for determining the number of signatures to use in classifying multispectral scanner data. A large initial set of signatures is obtained by clustering the training points within each category (such as 'wheat' or 'other') to be recognized. These clusters are then combined into broader signatures by a program that considers each pair of signatures within a category, combines the best pair in the light of certain criteria, saves the combined signature and repeats the procedure until there is one signature for each category. The result is a collection of sets of signatures, one set for each number between the number of initial clusters and the number of categories. With the aid of statistics such as an estimate of the probability of misclassification between categories, the user can choose the smallest set satisfying his requirements for classification accuracy.

Richardson, W.↗
NASA NTRS1976

Evaluation of classification procedures for estimating wheat acreage in Kansas

This report presents the results of experiments which were performed to evaluate procedures for estimating wheat acreage in intensive test sites (ITS's) in Kansas. An analyst/interpreter (AI) selected and labeled fields from Landsat-1 satellite imagery. Statistics were generated for each selected ITS, and the imagery was classified using a maximum likelihood classifier. Various components of the classification process were tested.

Flores, L. M.↗
NASA NTRS1979

Postprocessing classification images

Program cleans up remote-sensing maps. It can be used with existing image-processing software. Remapped images closely resemble familiar resource information maps and can replace or supplement classification images not postprocessed by this program.

Kan, E. P.↗
NASA NTRS1976

Land classification of south-central Iowa from computer enhanced images

The author has identified the following significant results. The Iowa Geological Survey developed its own capability for producing color products from digitally enhanced LANDSAT data. Research showed that efficient production of enhanced images required full utilization of both computer and photographic enhancement procedures. The 29 August 1972 photo-optically enhanced color composite was more easily interpreted for land classification purposes than standard color composites.

Lucas, J. R.↗
NASA NTRS1977

Computer-aided classification for remote sensing in agriculture and forestry in Northern Italy

A set of results concerning the processing and analysis of data from LANDSAT satellite and airborne scanner is presented. The possibility of performing inventories of irrigated crops-rice, planted groves-poplars, and natural forests in the mountians-beeches and chestnuts, is investigated in the Po valley and in an alphine site of Northern Italy. Accuracies around 95% or better, 70% and 60% respectively are achieved by using LANDSAT data and supervised classification. Discrimination of rice varieties is proved with 8 channels data from airborne scanner, processed after correction of the atmospheric effect due to the scanning angle, with and without linear feature selection of the data. The accuracies achieved range from 65% to more than 80%. The best results are obtained with the maximum likelihood classifier for normal parameters but rather close results are derived by using a modified version of the weighted euclidian distance between points, with consequent decrease in computing time around a factor 3.

Dejace, J.↗
NASA NTRS1978

Procedures for gathering ground truth information for a supervised approach to a computer-implemented land cover classification of LANDSAT-acquired multispectral scanner data

Procedures for gathering ground truth information for a supervised approach to a computer-implemented land cover classification of LANDSAT acquired multispectral scanner data are provided in a step by step manner. Criteria for determining size, number, uniformity, and predominant land cover of training sample sites are established. Suggestions are made for the organization and orientation of field team personnel, the procedures used in the field, and the format of the forms to be used. Estimates are made of the probable expenditures in time and costs. Examples of ground truth forms and definitions and criteria of major land cover categories are provided in appendixes.

Joyce, A. T.↗
NASA NTRS1978

Automatic classification of reforested Pinus SPP and Eucalyptus SPP in Mogi-Guacu, SP, Brazil, using LANDSAT data

The author has identified the following significant results. Single date LANDSAT CCTs were processed, by Image-100 to classify Pinus and Eucalyptus species and their age groups. The study area Mogi-Guagu was located in the humid subtropical climate zone of Sao Paulo. The study was divided into ten preliminary classes and featured selection algorithms were used to calculate Bhattacharyya distance between all possible pairs of these classes in the four available channels. Classes having B-distance values less than 1.30 were grouped in four classes: (1) class PE - P. elliottii, (2) class P0 - Pinus species other than P. elliotii, (3) class EY - Eucalyptus spp. under two years, and (4) class E0 - Eucalyptus spp. more than two years old. The percentages of correct classification ranged from 70.9% to 94.12%. Comparisons of acreage estimated from the Image-100 with ground truth data showed agreement. The Image-100 percent recognition values for the above four classes were 91.62%, 87.80%, 89.89%, and 103.30%, respectively.

Dejesusparada, N.↗
NASA NTRS1977

Satellite onboard feature classification using CCD's

In this paper discrete time analog signal processing applications of CCD's are discussed. The types of computations which can be performed in the CCD technology are discussed and compared with respect to their accuracy and required support hardware. The classification problem encountered in the reduction of multispectral data into features of terrestrial interest are decomposed into a CCD technology based hardware solution. The resulting system is readily implemented with existing and anticipated CCD devices. This system will be projected to spacecraft application.

Benz, H. F.↗
NASA NTRS1977

Application of Landsat data to wetland study and land use classification in West Tennessee

Landsat data were employed in determining land use of a 32,300-hectare watershed area within the Obion-Forked Deer River Basin in northwest Tennessee. Black and white transparency chips for all four wavelength bands were interpreted by use of a video-input analog/digital automatic analysis and classification facility; densitometric methods showed that wetlands, urban areas, agricultural lands and forests could be discriminated by analysis of band 6 or 7 together with band 4 or 5. Comparison with high- and low-altitude photography indicated that the Landsat data could provide sufficiently accurate resource information and determine drainage trends.

Jones, N. L.↗
NASA NTRS1977

Structured estimation - Sample size reduction for adaptive pattern classification

The Gaussian two-category classification problem with known category mean value vectors and identical but unknown category covariance matrices is considered. The weight vector depends on the unknown common covariance matrix, so the procedure is to estimate the covariance matrix in order to obtain an estimate of the optimum weight vector. The measure of performance for the adapted classifier is the output signal-to-interference noise ratio (SIR). A simple approximation for the expected SIR is gained by using the general sample covariance matrix estimator; this performance is both signal and true covariance matrix independent. An approximation is also found for the expected SIR obtained by using a Toeplitz form covariance matrix estimator; this performance is found to be dependent on both the signal and the true covariance matrix.

Morgera, S.↗
NASA NTRS1977

Landsat digital data application to forest vegetation and land use classification in Minnesota

Landsat digital data were used to map eleven categories of land cover in north central Minnesota. The classification accuracy of these maps was found to be very low and they were not adequate for use by field level resource managers. A discussion of the advantages and disadvantages of various processing systems, different algorithms, and the problems in selecting training sets, is included.

Mead, R. A.↗
NASA NTRS1977

Characterization of lunar mare basalt types. II - Spectral classification of fresh mare craters

Telescopic reflectance spectra (0.3-1.1 microns) of fresh craters are presented and classified according to the spectral features observed. These spectra are the closest lunar surface analog to laboratory spectra obtained for lunar rock powders. Mineral absorption features can be identified in these crater spectra and interpreted using returned lunar samples. Spectra of craters to 2.5 microns are required for specific mineralogical determinations from remote observation. If the currently available telescopic spectra are representative, classification of lunar telescopic spectra indicates that most (more than 80%) of the lunar surface is composed of a finite number of discrete and describable geochemical units.

Pieters, C.↗
NASA NTRS1978

Use of manual densitometry in land cover classification

Through use of manual spot densitometry values derived from multitemporal 1:24,000 color infrared aircraft photography, areas as small as one hectare in the Cumberland Plateau in Kentucky were accurately classified into one of eight ground cover groups. If distinguishing between undisturbed and disturbed forest areas is the sole criterion of interest, classification results are highly accurate if based on imagery taken during foliated ground cover conditions. Multiseasonal imagery analysis was superior to single data analysis, and transparencies from prefoliated conditions gave better separation of conifers and hardwoods than did those from foliated conditions.

Jordan, D. C.↗
NASA NTRS1979

Trophic classification of selected Colorado lakes

Multispectral scanner data, acquired over several Colorado lakes using LANDSAT-1 and aircraft, were used in conjunction with contact-sensed water quality data to determine the feasibility of assessing lacustrine trophic levels. A trophic state index was developed using contact-sensed data for several trophic indicators. Relationships between the digitally processed multispectral scanner data, several trophic indicators, and the trophic index were examined using a supervised multispectral classification technique and regression techniques. Statistically significant correlations exist between spectral bands, several of the trophic indicators and the trophic state index. Color-coded photomaps were generated which depict the spectral aspects of trophic state.

Blackwell, R. J.↗