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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Korean coastal water depth/sediment and land cover mapping (1:25,000) by computer analysis of LANDSAT imagery

Computer analysis was applied to single date LANDSAT MSS imagery of a sample coastal area near Seoul, Korea equivalent to a 1:50,000 topographic map. Supervised image processing yielded a test classification map from this sample image containing 12 classes: 5 water depth/sediment classes, 2 shoreline/tidal classes, and 5 coastal land cover classes at a scale of 1:25,000 and with a training set accuracy of 76%. Unsupervised image classification was applied to a subportion of the site analyzed and produced classification maps comparable in results in a spatial sense. The results of this test indicated that it is feasible to produce such quantitative maps for detailed study of dynamic coastal processes given a LANDSAT image data base at sufficiently frequent time intervals.

Park, K. Y.↗
NASA NTRS1980

LANDSAT signature development program

LANDSAT signature development program (LSDP) automatically produces unsupervised classification of scene from LANDSAT data tape. Program is effective enough to be useful to sophisticated remote sensing analyst yet is simple enough to be utilized by ground truth investigators who have only basic understanding of computer and remote sensing procedures.

Bland, R. A.↗
NASA NTRS1978

Correlation of chemistry with normal albedo in the Crisium region

The region in the vicinity of the Luna 24 handling site (40-70 deg E; 0-20 deg N) is studied by means of the Apollo orbital fluorescent X-ray Al/Si and Mg/Al data and earth-based normal albedo measurements. By means of unsupervised non-parametric cluster analysis this region is divided into eight surface units. One of these units centered in the Proclus region deviates from the usual trend of increasing normal albedo with increasing Al/Si values and decreasing Mg/Al values

Bielefeld, M. J.↗
NASA NTRS1981

Evaluating computer-drawn ground-cover maps

Computer-generated character maps from LANDSAT data are compared to aerial photos for test sites in Florida. Report Describes extraction of ground features by two analytical techniques: unsupervised clustering algorithm, called LANDSAT Signature Development Program (LSDP), and interactive algorithm based on multispectral image analyzer. Study concluded that computer classification of digital LANDSAT multispectral data, supplemented with certain ground-cover information, is valuable tool for analysis of renewable resources.

Arvanitis, L. G.↗
NASA NTRS1979

Clustering algorithm evaluation and the development of a replacement for procedure 1

An efficient procedure which clusters data using a completely unsupervised clustering algorithm and then uses labeled pixels to label the resulting clusters or perform a stratified estimate using the clusters as strata is developed. Three clustering algorithms, CLASSY, AMOEBA, and ISOCLS, are compared for efficiency. Three stratified estimation schemes and three labeling schemes are also considered and compared.

Lennington, R. K.↗
NASA NTRS1979

On the clustering of multidimensional pictorial data

Obvious approaches to reducing the cost (in computer resources) of applying current clustering techniques to the problem of remote sensing are discussed. The use of spatial information in finding fields and in classifying mixture pixels is examined, and the AMOEBA clustering program is described. Internally, a pattern recognition program, from without, AMOEBA appears to be an unsupervised clustering program. It is fast and automatic. No choices (such as arbitrary thresholds to set split/combine sequences) need be made. The problem of finding the number of clusters is solved automatically. At the conclusion of the program, all points in the scene are classified; however, a provision is included for a reject classification of some points which, within the theoretical framework, cannot rationally be assigned to any cluster.

Bryant, J. D.↗
NASA NTRS1980

A hill-sliding strategy for initialization of Gaussian clusters in the multidimensional space

A hill sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimate of sample data for the first step of iterative unsupervised classification. Each cluster was assumed to posses a unimodal normal distribution. A clustering function proposed distinguished elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with LANDSAT multispectral scanner data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill sliding clustering technique developed herein has the potential applicability to decomposition any multivariate mixture distribution into a number of unimodal distributions when an appropriate distribution function to the data set is employed.

Park, J. K.↗
NASA NTRS1979

Forest Classification and Inventory System using Landsat, digital terrain, and ground sample data

Accurate timber inventory data for cost-effective forest management is the primary goal of a Forest Classification and Inventory System (FOCIS) designed to provide estimates of timber volume by species aggregated by compartments, townships, or other spatial management units. FOCIS uses Landsat spectral data, including a synthesized texture channel, and Forest Service ground sample data to produce timber volume-homogeneous classes through a two-step unsupervised clustering process. Registered digital terrain data, including derived slope angle and slope aspect channels, are used to predict species proportions through a trend surface model, again using ground sample data for model calibration. In the final stage of FOCIS, volume estimates and predicted species proportions are merged and aggregated to yield timber volumes by species within management areas.

Strahler, A. H.↗
NASA NTRS1980

Korean coastal water depth/sediment and land cover mapping /1:25,000/ by computer analysis of Landsat imagery

Computer analysis was applied to single data Landsat MSS imagery of a coastal area near Seoul, Korea equivalent to a 1:50,000 topographic map, and featuring large dynamic sediment transport processes. Supervised image processing yielded a test classification map containing five water depth/sediment classes, two shoreline/tidal classes and five coastal land cover classes at a scale of 1:25,000 and with a training set accuracy of 76%; the training sets were selected by direct examination of the digitally displayed imagery. The unsupervised ISOCLAS (Senkus, 1976) clustering analysis was performed to assess the relative value of this approach to image classification in areas of sparse or nonexistent ground control. Results indicate that it is feasible to produce quantitative maps for detailed study of dynamic coastal processes given a Landsat image data base at sufficiently frequent time intervals.

Park, K. Y.↗
NASA NTRS1981

Evaluation of multiband, multitemporal, and transformed LANDSAT MSS data for land cover area estimation

Sample segments of ground-verified land cover data collected in conjunction with the USDA/ESS June Enumerative Survey were merged with LANDSAT data and served as a focus for unsupervised spectral class development and accuracy assessment. Multitemporal data sets were created from single-date LANDSAT MSS acquisitions from a nominal scene covering an eleven-county area in north central Missouri. Classification accuracies for the four land cover types predominant in the test site showed significant improvement in going from unitemporal to multitemporal data sets. Transformed LANDSAT data sets did not significantly improve classification accuracies. Regression estimators yielded mixed results for different land covers. Misregistration of two LANDSAT data sets by as much and one half pixels did not significantly alter overall classification accuracies. Existing algorithms for scene-to scene overlay proved adequate for multitemporal data analysis as long as statistical class development and accuracy assessment were restricted to field interior pixels.

Stoner, E. R.↗
NASA NTRS1980

Land-cover classification of California using mosaicking and high-speed processing

A land cover classification of Landsat MSS data has been made for the entire state of California. Digital mosaicking of 32 Landsat scenes with registered digital elevation, slope, and aspect was prepared. Stratified unsupervised clustering generated the spectral classes which were identified into 16 categories, mostly forest types, using U-2 CIR photos. A verification using 81 primary sample units resulted in a forest/nonforest accuracy of 80.2% and a commercial conifer and hardwood accuracy of 75.1%. The results are now being used in many projects throughout the state.

Peterson, D. L.↗
NASA NTRS1981

An evaluation of ISOCLS and CLASSY clustering algorithms for forest classification in northern Idaho

Both the iterative self-organizing clustering system (ISOCLS) and the CLASSY algorithms were applied to forest and nonforest classes for one 1:24,000 quadrangle map of northern Idaho and the classification and mapping accuracies were evaluated with 1:30,000 color infrared aerial photography. Confusion matrices for the two clustering algorithms were generated and studied to determine which is most applicable to forest and rangeland inventories in future projects. In an unsupervised mode, ISOCLS requires many trial-and-error runs to find the proper parameters to separate desired information classes. CLASSY tells more in a single run concerning the classes that can be separated, shows more promise for forest stratification than ISOCLS, and shows more promise for consistency. One major drawback to CLASSY is that important forest and range classes that are smaller than a minimum cluster size will be combined with other classes. The algorithm requires so much computer storage that only data sets as small as a quadrangle can be used at one time.

Werth, L. F.↗
NASA NTRS1981

Multiresource analysis and information system concepts for incorporating LANDSAT and GIS technology into large area forest surveys

A method of relating different classifications at each stage of a multistage, multiresource inventory using remotely sensed imagery is discussed. A class transformation matrix allowing the conversion of a set of proportions at one stage, to a set of proportions at the subsequent stage through use of a linear model, is described. The technique was tested by applying it to Kershaw County, South Carolina. Unsupervised LANDSAT spectral classifications were correlated with interpretations of land use aerial photography, the correlations employed to estimate land use classifications using the linear model, and the land use proportions used to stratify current annual increment (CAI) field plot data to obtain a total CAI for the county. The estimate differed by 1% from the published figure for land use. Potential sediment loss and a variety of land use classifications were also obtained.

Langley, P. G.↗
NASA NTRS1981

Application of remote sensing techniques for identification of irrigated crop lands in Arizona

Satellite imagery was used in a project developed to demonstrate remote sensing methods of determining irrigated acreage in Arizona. The Maricopa water district, west of Phoenix, was chosen as the test area. Band rationing and unsupervised categorization were used to perform the inventory. For both techniques the irrigation district boundaries and section lines were digitized and calculated and displayed by section. Both estimation techniques were quite accurate in estimating irrigated acreage in the 1979 growing season.

Billings, H. A.↗
NASA NTRS1982

Geometric and rediametric distortion in spaceborne SAR imagery

Distortions inherent on synthetic aperture radio (SAR) imagery and the development to date of unsupervised postprocessing rectification techniques are described. The geometric distortion can be divided into two categories: (1) distortion derived from the radar viewing geometry, this includes such effects as ground range nonlinearities, radar foreshortening and radar layover; (2) distortion introduced during the data processing, these distortions result from approximations made during the correlation such as in estimation of the target phase history, or compensation for the earth rotation. The processor induced distortions depends on the specific correlation algorithm used for image formation. The effects are addressed on the image product resulting from assumptions during the processing and it specifically considers distortions inherent in digital imagery produced by the digital image processor.

Curlander, J. C.↗
NASA NTRS1982

Image processing developments and applications for water quality monitoring and trophic state determination

Remote sensing data analysis of water quality monitoring is evaluated. Data anaysis and image processing techniques are applied to LANDSAT remote sensing data to produce an effective operational tool for lake water quality surveying and monitoring. Digital image processing and analysis techniques were designed, developed, tested, and applied to LANDSAT multispectral scanner (MSS) data and conventional surface acquired data. Utilization of these techniques facilitates the surveying and monitoring of large numbers of lakes in an operational manner. Supervised multispectral classification, when used in conjunction with surface acquired water quality indicators, is used to characterize water body trophic status. Unsupervised multispectral classification, when interpreted by lake scientists familiar with a specific water body, yields classifications of equal validity with supervised methods and in a more cost effective manner. Image data base technology is used to great advantage in characterizing other contributing effects to water quality. These effects include drainage basin configuration, terrain slope, soil, precipitation and land cover characteristics.

Blackwell, R. J.↗
NASA NTRS1981

Robots and manipulators

Robots are defined and described for various applications. The key feature of robots is programmability, which allows teleoperation, repair work in hazardous situations, and unsupervised operation in industrial functions. Two types of robots now exist: special purpose, with equipment for a specific task; and general purpose, which include nonservo-controlled robots, servo-controlled robots, and sensory control robots. Sensory robots are the most sophisticated, and are equipped with both internal control sensors and external sensors such as TV cameras, pressure detectors, laser range finders, etc. Sensory feedback to a central computer enables the robots to make appropriate modifications to the control program to adapt to new situations. Pattern recognition and scans for size are features of the TV sensors, and programs to develop a universal effector (hand) are outlined. Finally, robot programming in terms of manual, walkthrough, and textual methods are described, and the potential uses of robots for space and undersea construction and repair are discussed.

Heer, E.↗