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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Comparison of MSS and TM Data for Landcover Classification in the Chesapeake Bay Area: a Preliminary Report

An area bordering the Eastern Shore of the Chesapeake Bay was selected for study and classified using unsupervised techniques applied to LANDSAT-2 MSS data and several band combinations of LANDSAT-4 TM data. The accuracies of these Level I land cover classifications were verified using the Taylor's Island USGS 7.5 minute topographic map which was photointerpreted, digitized and rasterized. The the Taylor's Island map, comparing the MSS and TM three band (2 3 4) classifications, the increased resolution of TM produced a small improvement in overall accuracy of 1% correct due primarily to a small improvement, and 1% and 3%, in areas such as water and woodland. This was expected as the MSS data typically produce high accuracies for categories which cover large contiguous areas. However, in the categories covering smaller areas within the map there was generally an improvement of at least 10%. Classification of the important residential category improved 12%, and wetlands were mapped with 11% greater accuracy.

Mulligan, P. J.↗
NASA NTRS1983

Spaceborne SAR data for land-cover classification and change detection

Supervised maximum-likelihood classifications of Seasat, SIR-A, and Landsat pixel data demonstrated that SIR-A data provided the most accurate discrimination (72 percent) between five land-cover categories. Spatial averaging of the SAR data improved classification accuracy significantly due to a reduction in both fading and within-field variability. The best multichannel classification accuracy (97.5 percent) was achieved by combining the SIR-A data with two Seasat images (ascending and descending orbits). In addition, semiquantitative analysis of Seasat-A digital data shows that orbital SAR imagery can be successfully used for multitemporal detection of change related to hydrologic and agronomic conditions by using simple machine processing techniques.

Brisco, B.↗
NASA NTRS1983

Petrography and classification of refractory inclusions in the Allende and Mokoia CV3 chondrites

Results are reported for a comprehensive petrographic survey of the refractory inclusions in the Allende and Mokoia CV3 chondrites. More than 600 refractory inclusions in 22 thin sections of the meteorites were studied by optical and scanning-electron microscopy. Olivine-rich inclusions and Ca, Al-rich inclusions (CAIs) are aggregates of various combinations of three fundamental petrographic constituents: rimmed concentric objects, Ca, Si-rich chaotic material, and mafic inclusion matrix. A new classification system for refractory inclusions is developed that is based on the size and abundance of these three fundamental constituents. The new classification system avoids several problems that are inherent in other classification systems, which use the term 'coarse-grained' too restrictively for many simple CAIs and inaccurately for most mililite-rich complex CAIs.

Kornacki, A. S.↗
NASA NTRS1985

Evaluation of space SAR as a land-cover classification

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

Brisco, B.↗
NASA NTRS1985

Continental land cover classification using satellite data

Four different approaches to the classification of land cover for whole continents using multitemporal images of the normalized difference vegetation index derived from the Advanced Very High Resolution Radiometer of the NOAA series of satellites are discussed. The first approach uses only two dates from different seasons and classification dependent upon subdivision of the resultant two-dimensional feature space by an analyst using a track ball. The second approach involves a similar method of partitioning the feature space, but with the two dimensions being the first and second principal components derived from 13 four-week composite images. The third approach uses the maximum likelihood rule to derive the classified map. In the fourth approach, the amount of deviation from characteristic curves is used as a basis for classification.

Townshend, J. R. G.↗
NASA NTRS1984

Using spatial logic in classification of Landsat TM data

A strategy for spatial/spectral classification of Landsat TM data is presented. The strategy is founded upon 'spatial logic', a logic that seeks to emulate important aspects of visual image interpretation. The carefully structured classification process begins with spectral stratification of the data into water, vegetated and non-vegetated pixels. A region growing algorithm is then used to define 'fields' of similar land cover composition. Fields are characterized by cover composition, size and neighborhood characteristics. A supervised iterative contextual classification algorithm is developed to assign final land use/land cover labels. Maps are generalized using a spatial post-processing technique. Positive, though preliminary, results are presented.

Merchant, J. W.↗
NASA NTRS1984

A comparison of Landsat point and rectangular field training sets for land-use classification

Rectangular training fields of homogeneous spectroreflectance are commonly used in supervised pattern recognition efforts. Trial image classification with manually selected training sets gives irregular and misleading results due to statistical bias. A self-verifying, grid-sampled training point approach is proposed as a more statistically valid feature extraction technique. A systematic pixel sampling network of every ninth row and ninth column efficiently replaced the full image scene with smaller statistical vectors which preserved the necessary characteristics for classification. The composite second- and third-order average classification accuracy of 50.1 percent for 331,776 pixels in the full image substantially agreed with the 51 percent value predicted by the grid-sampled, 4,100-point training set.

Tom, C. H.↗
NASA NTRS1986

Space Shuttle cloud detection and earth feature classification experiment

The Feature Identification and Location Experiment (FILE) that is being designed for the detection and classification of four primary earth features (water, vegetation, bare land, and the clouds-snow-ice class) is described. Consideration is given to the FILE classification technology concept and the FILE instrument, which will use two solid-state CCD cameras operating at 0.65 and 0.85-micron center frequency wavelengths, with the camera outputs being functions of the earth surface material radiance. The classification is based on camera output radiance ratio values. The preliminary analysis of the data collected on the STS 41-G mission is discussed. The results demonstrated the suitability of using the two-channel-ratio detection technology and a simple (y = mx) algorithm to autonomously classify the four earth surface features. The technology is especially attractive as a cloud sensor, where, in advance of or during a mission, a threshold value for cloud cover percentage can be programmed and/or adaptively modified for use in the control of other remote sensors.

Sivertson, W. E., Jr.↗
NASA NTRS1988

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗
NASA NTRS1986

A proposed classification scheme for Ada-based software products

As the requirements for producing software in the Ada language become a reality for projects such as the Space Station, a great amount of Ada-based program code will begin to emerge. Recognizing the potential for varying levels of quality to result in Ada programs, what is needed is a classification scheme that describes the quality of a software product whose source code exists in Ada form. A 5-level classification scheme is proposed that attempts to decompose this potentially broad spectrum of quality which Ada programs may possess. The number of classes and their corresponding names are not as important as the mere fact that there needs to be some set of criteria from which to evaluate programs existing in Ada. An exact criteria for each class is not presented, nor are any detailed suggestions of how to effectively implement this quality assessment. The idea of Ada-based software classification is introduced and a set of requirements from which to base further research and development is suggested.

Cernosek, Gary J.↗
NASA NTRS1988

R-parametrization and its role in classification of linear multivariable feedback systems

A classification of all the compensators that stabilize a given general plant in a linear, time-invariant multi-input, multi-output feedback system is developed. This classification, along with the associated necessary and sufficient conditions for stability of the feedback system, is achieved through the introduction of a new parameterization, referred to as R-Parameterization, which is a dual of the familiar Q-Parameterization. The classification is made to the stability conditions of the compensators and the plant by themselves; and necessary and sufficient conditions are based on the stability of Q and R themselves.

Chen, Robert T. N.↗
NASA NTRS1989

Accuracy assessment, using stratified plurality sampling, of portions of a LANDSAT classification of the Arctic National Wildlife Refuge Coastal Plain

An application of a classification accuracy assessment procedure is described for a vegetation and land cover map prepared by digital image processing of LANDSAT multispectral scanner data. A statistical sampling procedure called Stratified Plurality Sampling was used to assess the accuracy of portions of a map of the Arctic National Wildlife Refuge coastal plain. Results are tabulated as percent correct classification overall as well as per category with associated confidence intervals. Although values of percent correct were disappointingly low for most categories, the study was useful in highlighting sources of classification error and demonstrating shortcomings of the plurality sampling method.

Card, Don H.↗
NASA NTRS1987

Evaluating Landsat classification accuracy from forest cover-type maps

The use of complete enumeration in the form of photointerpreted forest cover-type maps to evaluate the accuracy of Landsat classifications was compared with assessments made directly from the aerial photography. A computerized, geographic information system was utilized to compare the Landsat classifications with the cover-type maps on a pixel-by-pixel basis. Error maps of pixels which were similarly misclassified by a variety of algorithms contained a larger number of errors than were verified from the aerial photography. For the two test sites studied, only 67 and 52 percent of the pixels which were originally considered to be in error were substantiated as being in error. Discrepancies between the two results were primarily caused by definitional differences between the cover-type maps and the Landsat classifications, especially with regard to minimum-type size and crown closure estimates of forest land.

Hudson, W. D.↗
NASA NTRS1989

Automatic classification of spectra from the Infrared Astronomical Satellite (IRAS)

A new classification of Infrared spectra collected by the Infrared Astronomical Satellite (IRAS) is presented. The spectral classes were discovered automatically by a program called Auto Class 2. This program is a method for discovering (inducing) classes from a data base, utilizing a Bayesian probability approach. These classes can be used to give insight into the patterns that occur in the particular domain, in this case, infrared astronomical spectroscopy. The classified spectra are the entire Low Resolution Spectra (LRS) Atlas of 5,425 sources. There are seventy-seven classes in this classification and these in turn were meta-classified to produce nine meta-classes. The classification is presented as spectral plots, IRAS color-color plots, galactic distribution plots and class commentaries. Cross-reference tables, listing the sources by IRAS name and by Auto Class class, are also given. These classes show some of the well known classes, such as the black-body class, and silicate emission classes, but many other classes were unsuspected, while others show important subtle differences within the well known classes.

Cheeseman, Peter↗
NASA NTRS1989

The effect of spatial resolution upon texture-based cloud field classifications

The loss of cloud-classification accuracy as a function of spatial resolution is assessed by investigating the variation of textural measures as a function of spatial resolution. Landsat MSS imagery is progressively averaged to produce degraded imagery of 1/8-km, 1/4-km, 1/2-km, and 1-km spatial resolution, and textural measures are computed from the Gray Level Difference Vector (GLDV) approach described by Chen et al. (1989). It is found that the classification accuracies obtained using the 1/8-km spatial resolution data are similar to those obtained using the full-resolution (1/16 km) texture measures, indicating that there is no advantage in using even higher spatial resolution 30-m Landsat Thematic Mapper and 10-m SPOT imagery for cloud classification.

Welch, R. M.↗
NASA NTRS1989

Classification and knowledge

Automated procedures to classify objects are discussed. The classification problem is reviewed, and the relation of epistemology and classification is considered. The classification of stellar spectra and of resolved images of galaxies is addressed.

Kurtz, Michael J.↗
NASA NTRS1989

Cloud classification from satellite data using a fuzzy sets algorithm - A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗
NASA NTRS1989

Spectral band selection for classification of soil organic matter content

This paper describes the spectral-band-selection (SBS) algorithm of Chen and Landgrebe (1987, 1988, and 1989) and uses the algorithm to classify the organic matter content in the earth's surface soil. The effectiveness of the algorithm was evaluated comparing the results of classification of the soil organic matter using SBS bands with those obtained using Landsat MSS bands and TM bands, showing that the algorithm was successful in finding important spectral bands for classification of organic matter content. Using the calculated bands, the probabilities of correct classification for climate-stratified data were found to range from 0.910 to 0.980.

Henderson, Tracey L.↗