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NASA NTRS1993

Automated cloud classification with a fuzzy logic expert system

An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

Tovinkere, Vasanth↗
NASA NTRS1993

Acoustic target detection and classification using neural networks

A neural network approach to the classification of acoustic emissions of ground vehicles and helicopters is demonstrated. Data collected during the Joint Acoustic Propagation Experiment conducted in July of l991 at White Sands Missile Range, New Mexico was used to train a classifier to distinguish between the spectrums of a UH-1, M60, M1 and M114. An output node was also included that would recognize background (i.e. no target) data. Analysis revealed specific hidden nodes responding to the features input into the classifier. Initial results using the neural network were encouraging with high correct identification rates accompanied by high levels of confidence.

Robertson, James A.↗
NASA NTRS1993

Classification of high dimensional multispectral image data

A method for classifying high dimensional remote sensing data is described. The technique uses a radiometric adjustment to allow a human operator to identify and label training pixels by visually comparing the remotely sensed spectra to laboratory reflectance spectra. Training pixels for material without obvious spectral features are identified by traditional means. Features which are effective for discriminating between the classes are then derived from the original radiance data and used to classify the scene. This technique is applied to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data taken over Cuprite, Nevada in 1992, and the results are compared to an existing geologic map. This technique performed well even with noisy data and the fact that some of the materials in the scene lack absorption features. No adjustment for the atmosphere or other scene variables was made to the data classified. While the experimental results compare favorably with an existing geologic map, the primary purpose of this research was to demonstrate the classification method, as compared to the geology of the Cuprite scene.

Hoffbeck, Joseph P.↗
NASA NTRS1993

Classification of the LCVF AVIRIS test site with a Kohonen artificial neural network

We present a classification of an AVIRIS spectral image of the Lunar Crater Volcanic Field (LCVF). Geologic mapping from such data is made possible by distinctive mineral signatures: absorption features and the shape of the spectral continuum. The subtle spectral shape differences between some of the geological units in this scene along with the high dimensionality of the spectral presents a challenging pattern recognition task. We found an artificial neural network powerful in separating 13 geological units based on the full spectral resolution. The LCVF, in northern Nye County, Nevada, was the primary focus of the NASA-sponsored Geologic Remote Sensing Field Experiment in the summer of 1989. It consists of over 100 square miles of Quaternary basaltic pyroclastic and flow deposits. These deposits lie atop ignimbrites and silicic lava flows of Tertiary age and in turn are overlain by Quaternary alluvial and playa deposits. This AVIRIS image was collected on September 29, 1989 at 11:44 at 11:44 PDT. The 256-by-256 pixel subsection in this study contains oxidized basaltic cinder deposits, the southern half of the Lunar Lake playa, and outcrops of the Rhyollite of Big Sand Spring Valley. Vegetation in LCVF is sparse, but locally abundant within washes and near springs.

Merenyi, Erzsebet↗
NASA NTRS1993

Classification and soil moisture determination of agricultural fields

During the Mac-Europe campaign of 1991 several SAR (Synthetic Aperature Radar) experiments were carried out in the Flevoland test area in the Netherlands. The test site consists of a forested and an agricultural area with more than 15 different crop types. The experiments took place in June and July (mid to late growing season). The area was monitored by the spaceborne C-band VV polarized ERS-1, the Dutch airborne PHARS with similar frequency and polarization and the three-frequency PP-, L-, and C-band) polarimetric AIRSAR system of NASA/JPL. The last system passed over on June 15, 3, 12, and 28. The last two dates coincided with the overpasses of the PHARS and the ERS-1. Comparison of the results showed that backscattering coefficients from the three systems agree quite well. In this paper we present the results of a study of crop type classification (section 2) and soil moisture determination in the agricultural area (section 3). For these studies we used field averaged Stokes matrices extracted from the AIRSAR data (processor version 3.55 or 3.56).

Vandenbroek, A. C.↗
NASA NTRS1995

Terrain classification using circular polarimetric features

Conventional representations of polarization response are referred to a horizontally and vertically polarized basis. Recent studies by Freeman and Durden, van Zyl, and others suggest that alternative polarimetric features which more easily resolve the contributions of simple scattering mechanisms such as odd-bounce, even-bounce, and diffuse scattering could offer several advantages in terrain classification. The circular polarization covariance matrix is a potential source of such features. In this paper, we derive its relationship to the Stokes matrix, describe some of its properties, and compare the utility of linear and circular polarimetric features in classifying an AIRSAR scene containing urban, park, and ocean terrain.

Michelson, David G.↗
NASA NTRS1994

The classification of the Arctic Sea ice types and the determination of surface temperature using advanced very high resolution radiometer data

The accurate quantification of new ice and open water areas and surface temperatures within the sea ice packs is a key to the realistic parameterization of heat, moisture, and turbulence fluxes between ocean and atmosphere in the polar regions. Multispectral NOAA advanced very high resolution radiometer/2 (AVHRR/2) satellite images are analyzed to evaluate how effectively the data can be used to characterize sea ice in the Bering and Greenland seas, both in terms of surface type and physical temperature. The basis of the classification algorithm, which is developed using a late wintertime Bering Sea ice cover data, is that frequency distributions of 10.8- micrometers radiances provide four distinct peaks, represeting open water, new ice, young ice, and thick ice with a snow cover. The results are found to be spatially and temporally consistent. Possible sources of ambiguity, especially associated with wider temporal and spatial application of the technique, are discussed. An ice surface temperature algorithm is developed for the same study area by regressing thermal infrared data from 10.8- and 12.0- micrometers channels against station air temperatures, which are assumed to approximate the skin temperatures of adjacent snow and ice. The standard deviations of the results when compared with in situ data are about 0.5 K over leads and polynyas to about 0.5-1.5 K over thick ice. This study is based upon a set of in situ data limited in scope and coverage. Cloud masks are applied using a thresholding technique that utilizes 3.74- and 10.8- micrometers channel data. The temperature maps produced show coherence with surface features like new ice and leads, and consistency with corresponding surface type maps. Further studies are needed to better understand the effects of both the spatial and temporal variability in emissivity, aerosol and precipitable atmospheric ice particle distribution, and atmospheric temperature inversions.

Massom, Robert↗
NASA NTRS1996

Software For Multivariate Bayesian Classification

PHD general-purpose classifier computer program. Uses Bayesian methods to classify vectors of real numbers, based on combination of statistical techniques that include multivariate density estimation, Parzen density kernels, and EM (Expectation Maximization) algorithm. By means of simple graphical interface, user trains classifier to recognize two or more classes of data and then use it to identify new data. Written in ANSI C for Unix systems and optimized for online classification applications. Embedded in another program, or runs by itself using simple graphical-user-interface. Online help files makes program easy to use.

Saul, Ronald↗
NASA NTRS1995

Geophysical phenomena classification by artificial neural networks

Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

Gough, M. P.↗
NASA NTRS1994

A new machine classification method applied to human peripheral blood leukocytes

Human beings judge images by complex mental processes, whereas computing machines extract features. By reducing scaled human judgments and machine extracted features to a common metric space and fitting them by regression, the judgments of human experts rendered on a sample of images may be imposed on an image population to provide automatic classification.

Rorvig, Mark E.↗
NASA NTRS1996

Classification and designation systems for materials: A report on the present situation, an inventory of the systems in use, and comments on the future possibilities

The different systems that are in use for the major classes of engineering materials are summarized. The work was carried out within the scope of the Versailles project on advanced materials and standards (VAMAS). An inventory of national and international standards that give specifications for the materials and international standards are included. Comments on the increasing knowledge of, and the increasing demand for, data concerning the materials performance are included. Recommendations for future activities in the standardization of classification and designation systems are given.

Reynard, Keith W.↗
NASA NTRS1996

On the Classification of Infrared Spectra from Circumstellar Dust Shells

We present results from an ongoing effort to classify the infrared spectra produced by circumstellar dust shells. Earlier efforts concentrated on oxygen-rich dust shells from sources associated with the asymptotic giant branch (AGB). Here, we describe the expansion of our classification to include S stars, supergiants, and carbon stars.

Sloan, G. C.↗
NASA NTRS1993

Decision Boundary Feature Extraction for Nonparametric Classification

Feature extraction has long been an important topic in pattern recognition. Although many authors have studied feature extraction for parametric classifiers, relatively few feature extraction algorithms are available for nonparametric classifiers. A new feature extraction algorithm based on decision boundaries for nonparametric classifiers is proposed. It is noted that feature extraction for pattern recognition is equivalent to retaining 'discriminantly informative features' and a discriminantly informative feature is related to the decision boundary. Since nonparametric classifiers do not define decision boundaries in analytic form, the decision boundary and normal vectors must be estimated numerically. A procedure to extract discriminantly informative features based on a decision boundary for non-parametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.

Lee, Chulhee↗
NASA NTRS1996

Global Single and Multiple Cloud Classification with a Fuzzy Logic Expert System

An unresolved problem in remote sensing concerns the analysis of satellite imagery containing both single and multiple cloud layers. While cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget, most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. Coakley (1983) used a spatial coherence method to determine whether a region contained more than one cloud layer. Baum et al. (1995) developed a scheme for detection and analysis of daytime multiple cloud layers using merged AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High-resolution Infrared Radiometer Sounder) data collected during the First ISCCP Regional Experiment (FIRE) Cirrus 2 field campaign. Baum et al. (1995) explored the use of a cloud classification technique based on AVHRR data. This study examines the feasibility of applying the cloud classifier to global satellite imagery.

Welch, Ronald M.↗
NASA NTRS1999

Stellar C III Emissions as a New Classification Parameter for (WC) Central Stars

We report detection of stellar C III lambda 1909 emission in International Ultraviolet Explorer (IUE) echelle spectra of early-type [WC] planetary nebula central stars (CSPNs). Additionally, stellar C III emission at lambda 2297 is observed in early- and late-type [WC) CSPNS. Inclusion of these C III features for abundance determinations may resolve a conflict of underabundance of C/O for early type [WC2] - [WC4] CSPNS. A linear dependence on stellar C III lambda 2297 equivalent widths can be used to indicate a new classification of type [WCUV] central stars.

Feibelman, W. A.↗
NASA NTRS1999

An Intercomparison Between Radar Reflectivity and the IR Cloud Classification Technique for the TOGA-COARE Area

Satellite infrared (IR) and visible (VIS) images from the Tropical Ocean Global Atmosphere - Coupled Ocean Atmosphere Response Experiment (TOGA-COARE) experiment are investigated through the use of Clustering Analysis. The clusters are obtained from the values of IR and VIS counts and the local variance for both channels. The clustering procedure is based on the standardized histogram of each variable obtained from 179 pairs of images. A new approach to classify high clouds using only IR and the clustering technique is proposed. This method allows the separation of the enhanced convection in two main classes: convective tops, more closely related to the most active core of the storm, and convective systems, which produce regions of merged, thick anvil clouds. The resulting classification of different portions of cloudiness is compared to the radar reflectivity field for intensive events. Convective Systems and Convective Tops are followed during their life cycle using the IR clustering method. The areal coverage of precipitation and features related to convective and stratiform rain is obtained from the radar for each stage of the evolving Mesoscale Convective Systems (MCS). In order to compare the IR clustering method with a simple threshold technique, two IR thresholds (Tir) were used to identify different portions of cloudiness, Tir=240K which roughly defines the extent of all cloudiness associated with the MCS, and Tir=220K which indicates the presence of deep convection. It is shown that the IR clustering technique can be used as a simple alternative to identify the actual portion of convective and stratiform rainfall.

Carvalho, L. M. V.↗
NASA NTRS1999

Implementation of Multispectral Image Classification on a Remote Adaptive Computer

As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

Figueiredo, Marco A.↗
NASA NTRS1994

An Improved Automatic Classification of a Landsat/TM Image from Kansas (FIFE)

This research note shows the results of applying a new massively parallel version of the automatic classification program (AutoClass IV) to a particular Landsat/TM image. The previous results for this image were produced using a "subsampling" technique because of the image size. The new massively parallel version of AutoClass allows the complete image to be classified without "subsampling", thus yielding improved results. The area in question is the FIFE study area in Kansas, and the classes AutoClass found show many interesting subtle variations in types of ground cover. Displays of the spatial distributions of these classes make up the bulk of this report. While the spatial distribution of some of these classes make their interpretation easy, most of the classes require detailed knowledge of the area for their full interpretation. We hope that some who receive this document can help us in understanding these classes. One of the motivations of this exercise was to test the new version of AutoClass (IV) that allows for correlation among the variables within a class. The scatter plots associated with the classes show that this correlation information is important in separating the classes. The fact that the spatial distribution of each of these classes is far from uniform, even though AutoClass was not given information about positions of pixels, shows that the classes are due to real differences in the image.

Kanefsky, Bob↗