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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Preparation of urban land use inventories by machine-processing of ERTS MSS data

Spectral classes of urban phenomena identified from ERTS multispectral scanner data in Milwaukee included Surburban, Inner City, Industry, Grassy (open area), Road, Wooded Suburb, Water, Cloud, and Shadow. The Milwaukee spectral class statistics were used to classify the Chicago area, within the same ERTS frame, and similar results were achieved. In another ERTS frame, Marion County (Indianapolis) data were classified into similar classes. The Marion County ERTS study was supported by a land use classification of an area near downtown Indianapolis that utilized 12-band MSS data collected by aircraft from 3000 feet. The results of the ERTS analyses suggest that satellite data will be a useful tool for the urban planner for monitoring urban land use.

Todd, W.↗
NASA NTRS1973

Preparation of urban land use inventories by machine processing of ERTS MSS data

Spectral classes of urban phenomena identified from Earth Resources Technology Satellite (ERTS) multispectral scanner data in Milwaukee included suburban inner city, industry, grassy (open area), road, wooded suburb, water cloud, and shadow. The Milwaukee spectral class statistics were used to classify the Chicago area, within the same ERTS frame, and similar results were achieved. In another ERTS frame, Marion County (Indianapolis) data were classified into similar classes. The Marion County ERTS study was supported by a land use classification of an area near downtown Indianapolis that utilized 12-band MSS data collected by aircraft from 3000 feet. The results of the ERTS analyses suggest that satellite data will be a useful tool for the urban planner for monitoring urban land use.

Todd, W.↗
NASA NTRS1974

Machine processing for remotely acquired data

This paper is a general discussion of earth resources information systems which utilize airborne and spaceborne sensors. It points out that information may be derived by sensing and analyzing the spectral, spatial and temporal variations of electromagnetic fields emanating from the earth surface. After giving an overview system organization, the two broad categories of system types are discussed. These are systems in which high quality imagery is essential and those more numerically oriented. Sensors are also discussed with this categorization of systems in mind. The multispectral approach and pattern recognition are described as an example data analysis procedure for numerically-oriented systems. The steps necessary in using a pattern recognition scheme are described and illustrated with data obtained from aircraft and the Earth Resources Technology Satellite (ERTS-1).

Landgrebe, D. A.↗
NASA NTRS1975

Snow cover monitoring by machine processing of multitemporal LANDSAT MSS data

LANDSAT frames were geometrically corrected and data sets from six different dates were overlaid to produce a 24 channel (six dates and four wavelength bands) data tape. Changes in the extent of the snowpack could be accurately and easily determined using a change detection technique on data which had previously been classified by the LARSYS software system. A second phase of the analysis involved determination of the relationship between spatial resolution or data sampling frequency and accuracy of measuring the area of the snowpack.

Luther, S. G.↗
NASA NTRS1975

Snowcover mapping by machine processing of Skylab and LANDSAT MSS data

Skylab and LANDSAT MSS data were analyzed using computer-aided analysis techniques (CAAT). Results indicated that the middle infrared wavelength bands of the Skylab S-192 scanner would allow effective discrimination between snowcover and water-droplet clouds, whereas the limited spectral response of the LANDSAT-1 or 2 scanners do not allow such spectral discrimination. Five spectral classes of snowcover were defined and mapped. These classes were found to be related to differences in the proportion of snow and forest cover in the individual resolution elements.

Bartolucci, L. A.↗
NASA NTRS1976

An analysis of metropolitan land-use by machine processing of earth resources technology satellite data

A successful application of state-of-the-art remote sensing technology in classifying an urban area into its broad land use classes is reported. This research proves that numerous urban features are amenable to classification using ERTS multispectral data automatically processed by computer. Furthermore, such automatic data processing (ADP) techniques permit areal analysis on an unprecedented scale with a minimum expenditure of time. Also, classification results obtained using ADP procedures are consistent, comparable, and replicable. The results of classification are compared with the proposed U. S. G. S. land use classification system in order to determine the level of classification that is feasible to obtain through ERTS analysis of metropolitan areas.

Mausel, P. W.↗
NASA NTRS1976

Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Ind., June 29-July 1, 1976, Proceedings

Papers are presented on the applicability of Landsat data to water management and control needs, IBIS, a geographic information system based on digital image processing and image raster datatype, and the Image Data Access Method (IDAM) for the Earth Resources Interactive Processing System. Attention is also given to the Prototype Classification and Mensuration System (PROCAMS) applied to agricultural data, the use of Landsat for water quality monitoring in North Carolina, and the analysis of geophysical remote sensing data using multivariate pattern recognition. The Illinois crop-acreage estimation experiment, the Pacific Northwest Resources Inventory Demonstration, and the effects of spatial misregistration on multispectral recognition are also considered. Individual items are announced in this issue.

Source record↗
NASA NTRS1976

Machine processing of remotely acquired data

The two major branches of remote sensing are based on image orientation and numerical orientation. Numerically oriented systems tend to involve computers for data analysis. In designing an information-gathering system, the sensor as well as the means of analysis must be well mated to the type of system orientation. Attention is given to sensor types as related to system types, the multispectral approach and pattern recognition, the multispectral scanner as data source, an illustrative example, procedural details in the use of pattern recognition, the speed and cost of data processing, the use of spatial information, and data preprocessing steps.

Landgrebe, D. A.↗
NASA NTRS1977

Estimating costs and performance of systems for machine processing of remotely sensed data

This paper outlines a method for estimating computer processing times and costs incurred in producing information products from digital remotely sensed data. The method accounts for both computation and overhead, and may be applied to any serial computer. The method is applied to estimate the cost and computer time involved in producing Level II Land Use and Vegetative Cover Maps for a five-state midwestern region. The results show that the amount of data to be processed overloads some example computer systems, but that the processing is feasible on others.

Ballard, R. J.↗
NASA NTRS1979

Machine processing of Landsat MSS data and DMA topographic data for forest cover type mapping

A study with the objective of developing and testing techniques which utilize both digital topographic data and Landsat MSS spectral data to map forest cover types is examined. Emphasis is given to the topographic distribution model (TDM), which combines point-by-point information about forest species, elevation, slope, and aspect to quantitatively describe topographic positions. Results show the stratified random sample approach to be very effective for developing the TDM, while the use of topographic data significantly improved the overall classification accuracy of forest cover types as compared to using spectral data alone.

Fleming, M. D.↗
NASA NTRS1991

Farley Three-Dimensional-Braiding Machine

Process and device known as Farley three-dimensional-braiding machine conceived to fabricate dry continuous fiber-reinforced preforms of complex three-dimensional shapes for subsequent processing into composite structures. Robotic fiber supply dispenses yarn as it traverses braiding surface. Combines many attributes of weaving and braiding processes with other attributes and capabilities. Other applications include decorative cloths, rugs, and other domestic textiles. Concept could lead to large variety of fiber layups and to entirely new products as well as new fiber-reinforcing applications.

Farley, Gary L.↗
NASA NTRS1976

Urban land use monitoring from computer-implemented processing of airborne multispectral data

Machine processing techniques were applied to multispectral data obtained from airborne scanners at an elevation of 600 meters over central Indianapolis in August, 1972. Computer analysis of these spectral data indicate that roads (two types), roof tops (three types), dense grass (two types), sparse grass (two types), trees, bare soil, and water (two types) can be accurately identified. Using computers, it is possible to determine land uses from analysis of type, size, shape, and spatial associations of earth surface images identified from multispectral data. Land use data developed through machine processing techniques can be programmed to monitor land use changes, simulate land use conditions, and provide impact statistics that are required to analyze stresses placed on spatial systems.

Todd, W. J.↗