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At least 217 records · Page 12

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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.↗

NASA NTRS1991

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data source. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Kim, H.↗

NASA NTRS1990

A neural network approach to cloud classification

It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery achieves an overall cloud identification accuracy of 93 percent. Cirrus can be distinguished from boundary layer cloudiness with an accuracy of 96 percent, without the use of an infrared channel. Stratocumulus is retrieved with an accuracy of 92 percent, cumulus at 90 percent. The use of the neural network does not improve cirrus classification accuracy. Rather, its main effect is in the improved separation between stratocumulus and cumulus cloudiness. While most cloud classification algorithms rely on linear parametric schemes, the present study is based on a nonlinear, nonparametric four-layer neural network approach. A three-layer neural network architecture, the nonparametric K-nearest neighbor approach, and the linear stepwise discriminant analysis procedure are compared. A significant finding is that significantly higher accuracies are attained with the nonparametric approaches using only 20 percent of the database as training data, compared to 67 percent of the database in the linear approach.

Lee, Jonathan↗

NASA NTRS1989

Feature utility in polarimetric radar image classification

The information content in polarimetric SAR images is examined, and the polarimetric image variables containing the information that is important to the classification of terrain features in the images are determined. It is concluded that accurate classification can be done when just over half of the image variables are retained. A reduction in image data dimensionality gives storage savings, and can lead to the improvement of classifier performance. In addition, it is shown that a simplified radar system with only phase-calibrated CO-POL or SINGLE TX channels can give classification performance which approaches that of a fully polarimetric radar.

Cumming, Ian G.↗

NASA NTRS1990

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data sources. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Kim, H.↗

NASA NTRS1992

Iterative Bayesian Classification In Polarimetric SAR

In improved scheme for Bayesian classification of picture elements in polarimetric synthetic-aperture radar image of terrain, priori probability that given picture element belongs to given class, adjusted according to spatial variation of statistical properties of image data. Accuracy increases dramatically in first few iterations. Scheme involves sequence of classifications. In first, a priori probability that element belongs to class taken to be constant over the whole image. In subsequent classifications, adaptive a priori probabilities calculated for each picture element.

Van Zyl, Jakob J.↗

NASA NTRS1992

Design of neural networks for classification of remotely sensed imagery

Classification accuracies of a backpropagation neural network are discussed and compared with a maximum likelihood classifier (MLC) with multivariate normal class models. We have found that, because of its nonparametric nature, the neural network outperforms the MLC in this area. In addition, we discuss techniques for constructing optimal neural nets on parallel hardware like the MasPar MP-1 currently at GSFC. Other important discussions are centered around training and classification times of the two methods, and sensitivity to the training data. Finally, we discuss future work in the area of classification and neural nets.

Chettri, Samir R.↗