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All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Principal components technique analysis for vegetation and land use discrimination

Automatic pre-processing technique called Principal Components (PRINCO) in analyzing LANDSAT digitized data, for land use and vegetation cover, on the Brazilian cerrados was evaluated. The chosen pilot area, 223/67 of MSS/LANDSAT 3, was classified on a GE Image-100 System, through a maximum-likehood algorithm (MAXVER). The same procedure was applied to the PRINCO treated image. PRINCO consists of a linear transformation performed on the original bands, in order to eliminate the information redundancy of the LANDSAT channels. After PRINCO only two channels were used thus reducing computer effort. The original channels and the PRINCO channels grey levels for the five identified classes (grassland, "cerrado", burned areas, anthropic areas, and gallery forest) were obtained through the MAXVER algorithm. This algorithm also presented the average performance for both cases. In order to evaluate the results, the Jeffreys-Matusita distance (JM-distance) between classes was computed. The classification matrix, obtained through MAXVER, after a PRINCO pre-processing, showed approximately the same average performance in the classes separability.

Parada, N. D. J.↗
NASA NTRS1984

Geobotanical discrimination of ultramafic parent materials An evaluation of remote sensing techniques

Color and color infrared aerial photography and imagery acquired from a Daedalus DEI-1260 multispectral airborne scanner were employed in an investigation to discriminate ultramafic rock types in a test site in southwest Oregon. An analysis of the relationships between vegetation characteristics and parent materials was performed using a vegetation classification and map developed for the project, lithologic information derived from published geologic maps of the region, and terrain information gathered in the field. Several analytical methods, including visual image analysis, band ratioing, principal components analysis, and contrast enhancement and subsequent color composite generation were used in the investigation. There was a close correspondence between vegetation types and major rock types. These were readily discriminated by the remote sensing techniques. It was found that ultramafic rock types were separable from non-ultramafic rock types and serpentine was distinguishable from non-serpentinized peridotite. Further investigations involving spectroradiometric and digital classification techniques are being performed to further identify rock types and to discriminate chromium and nickel-bearing rock types.

Mouat, D. A.↗
NASA NTRS1992

Lidar conversion parameters derived from SAGE II extinction measurements

SAGE II multiwavelength aerosol extinction measurements are used to estimate mass- and extinction-to-backscatter conversion parameters. The basis of the analysis is the principal component analysis of the SAGE II extinction kernels to estimate both total aerosol mass and aerosol backscatter at a variety of wavelengths. Comparisons of coincident SAGE II extinction profiles with 0.694-micron aerosol backscatter profiles demonstrate the validity of the method.

Thomason, L. W.↗
NASA NTRS1984

Quantification of waste morphology in Martian fretted terrain

Qualitative and quantitative analyses are performed on the northern fretted terrain of the Martian Isnenius Lacus quadrangle to determine the formation processes. The fretted terrain lies between 35-50 deg N latitude and 305-350 deg W longitude and was mapped with Viking orbiter instrumentation. The dominant landforms are a sequence of heavily cratered terrain, scattered flat-floored, fretted valleys, linear waste-covered valleys between flat-topped mesas, circum-mesa debris areas, a transition from mesas to knobby terrain, fewer upland forms, and low-relief, mantled, polar plains. The features spread out from a cratered terrain boundary in a succession of shapes. A principal components analysis characterizes definable spatial variations in the surface morphology and suggests that the features are youngest in the southwest direction. Relationships are also found between neighboring features. The principal components analysis technique is concluded to be a valuable tool for explaining the geomorphic evolution of the Martian surface.

Kochel, R. C.↗
NASA NTRS1983

LANDSAT-D thematic mapper image dimensionality reduction and geometric correction accuracy

When principal component analysis of a subscene of a section of the Sacramento River showed lower correlation among the TM spectral components that were observed for the MSS spectral components, principal component analysis was applied to a LANDSAT 2 MSS subscene of the same area for comparison purposes. Correlation coefficient matrices indicate the pairwise similarity and correlation of the data for the spectral components. The principal components transformation matrix, indicates the weights applied to the original components to generate the transformed components. The first two TM components can be described as visible and near infrared. For the MSS data, the first transformed component is roughly the average of the four original components. The second transformed component is roughly the difference between the visible and infrared components. Tables show that 97.0% of the variance in an MSS image is contained in only two transformed components.

Ford, G. E.↗
NASA NTRS1983

Shuttle Imaging Radar-A (SIR-A) data as a complement to Landsat Multispectral Scanner (MSS) data

Principal components analysis and supervised classifications were performed on two dates of Landsat multispectral scanner (MSS) data registered to one date of Shuttle Imaging Radar-A (SIR-A) data in a wheat-growing area of New South Wales, Australia. The purpose was to evaluate SIR-A data as a complement to Landsat MSS data in an agricultural environment. The SIR-A data was filtered using a 7 x 7 pixel moving window median filter. Principal components analysis indicated the SIR-A data were discriminating between trees and agricultural fields. Supervised classifications using wheat, pasture, trees, and idle classes resulted in increased accuracies for wheat and pasture and slightly decreased accuracies for trees and idle for the Landsat MSS/SIR-A registered data sets over the Landsat MSS alone. Overall classification accuracies were unchanged for one date and substantially increased for the other when the SIR-A data were added to the Landsat MSS data.

Henninger, D. L.↗
NASA NTRS1982

LANDSAT-4 image data quality analysis

Work done on evaluating the geometric and radiometric quality of early LANDSAT-4 sensor data is described. Band to band and channel to channel registration evaluations were carried out using a line correlator. Visual blink comparisons were run on an image display to observe band to band registration over 512 x 512 pixel blocks. The results indicate a .5 pixel line misregistration between the 1.55 to 1.75, 2.08 to 2.35 micrometer bands and the first four bands. Also a four 30M line and column misregistration of the thermal IR band was observed. Radiometric evaluation included mean and variance analysis of individual detectors and principal components analysis. Results indicate that detector bias for all bands is very close or within tolerance. Bright spots were observed in the thermal IR band on an 18 line by 128 pixel grid. No explanation for this was pursued. The general overall quality of the TM was judged to be very high.

Anuta, P. E.↗
NASA NTRS1992

Adaptive filtering and prediction of the Southern Oscillation index

Singular spectrum analysis (SSA), a variant of principal component analysis, is applied to a time series of the Southern Oscillation index (SOI). The analysis filters out variability unrelated to the Southern Oscillation and separates the high-frequency, 2- to 3-year variability, including the quasi-biennial oscillation, from the lower-frequency 4- to 6-year El Nino cycle. The maximum entropy method (MEM) is applied to forecasting the prefiltered SOI. Prediction based on MEM-associated autoregresive models has useful skill for 30-36 months. A 1993-1994 La Nina event is predicted based on data through February 1992.

Keppenne, Christian L.↗
NASA NTRS1977

Image analysis techniques with special reference to analysis and interpretation of geological features from LANDSAT imagery

The principal component analysis enhances the contrast existing between the different cover types present in an imagery. A procedure is presented with regards to the determination of the principal components. The method is tested for a portion of the LANDSAT imagery pertaining to Anantapur region. Another technique, using the concept of non-linear contrast stretching is defined and developed and carried out on the same imagery. The results are presented as photographs. An interpretation of the geology of the region is derived from these photographs.

Kamat, D. S.↗
NASA NTRS1986

The use of multidate multichannel radiance data in urban feature analysis

Two images were obtained from thematic mappers on Landsats 4 and 5 over the Washington, DC area during November 1982 and March 1984. Selected training areas containing different types of urban land use were examined,one area consisting entirely of forest. Mean digital radiance values for each bandpass in each image were examined, and variances, standard deviations, and covariances between bandpasses were calculated. It has been found that two bandpasses caused forested areas to stand out from other land use types, especially for the November 1982 image. In order to evaluate quantitatively the possible utility of the principal components analysis in selected feature extraction, the eigenvectors were evaluated for principal axes rotations which rendered each selected land use type most separable from all other land use types. The evaluated eigenvectors were plotted as a function of land use type, whose order was decided by considering anticipated shadow component and by examining the relative loadings indicative of vegetation for each of the principal components for the different features considered. The analysis was performed for each seven-band image separately and for the two combined images. It was found that by combining the two images, more dramatic land use type separation could be obtained.

Duggin, M. J.↗
NASA NTRS1993

Fourier domain target transformation analysis in the thermal infrared

Remote sensing uses of principal component analysis (PCA) of multispectral images include band selection and optimal color selection for display of information content. PCA has also been used for quantitative determination of mineral types and abundances given end member spectra. The preliminary results of the investigation of target transformation PCA (TTPCA) in the fourier domain to both identify end member spectra in an unknown spectrum, and to then calculate the relative concentrations of these selected end members are presented. Identification of endmember spectra in an unknown sample has previously been performed through bandmatching, expert systems, and binary classifiers. Both bandmatching and expert system techniques require the analyst to select bands or combinations of bands unique to each endmember. Thermal infrared mineral spectra have broad spectral features which vary subtly with composition. This makes identification of unique features difficult. Alternatively, whole spectra can be used in the classification process, in which case there is not need for an expert to identify unique spectra. Use of binary classifiers on whole spectra to identify endmember components has met with some success. These techniques can be used, along with a least squares fit approach on the endmembers identified, to derive compositional information. An alternative to the approach outlined above usese target transformation in conjunction with PCA to both identify and quantify the composition of unknown spectra. Preprocessing of the library and unknown spectra into the fourier domain, and using only a specific number of the components, allows for significant data volume reduction while maintaining a linear relationship in a Beer's Law sense. The approach taken here is to iteratively calculate concentrations, reducing the number of endmember components until only non-negative concentrations remain.

Anderson, D. L.↗
NASA NTRS1994

The intermediate line region of QSOs

Our recent statistical investigations of broad UV lines of luminous quasars (QSOs) suggest that the traditional broadline region (BLR) consists of two components-one of width approximately 2000 km/s FWHM with the peak within a few hundred kilometers per second of the systemic redshift, and another very broad component of width greater than or equal to 7000 km/s FWHM and blueshifted by greater than or equal to 1000 km/s. Differences in the relative strengths of these components account for much of the diversity of broad-line profiles, as well as relations among line strength, line width, asymmetry, and peak blueshift. We have suggested that the narrower component arises in a distinct intermediate-line region (ILR) that is an inner extension of the narrow-line region (NLR). We form spectra of the ILR and very broad line region (VBLR) in two complementary ways. First, using a small sample of high-quality spectra, we difference two composite spectra, one with FWHM (sub C IV) approximately 3000 km/s, the other with FWHM (sub C IV) approximately 7000 km/s (essentially a VBLR spectrum)-revealing a narrower line spectrum with FWHM approximately 2000 km/s. Second, we use a principal component analysis of 200 low signal-to-noise ratio spectra from the Large Bright Quasar Survey. The ILR is identified with the first principal component-that component accounting for most of the spectrum-to-spectrum variation. The VBLR spectrum is derived by subtracting this ILR from the mean spectrum. The two methods yield similar results, and the spectra of the ILR and VBLR are very different. Additional support for the existence of two component is the lack of a correlation between the equivalent widths of the ILR and VBLR. We discuss the relationships between the VBLR, the ILR, and the traditional NLR. The ILR line intensity ratios are distinctly different from those of the VBLR, with (relative to C IV lambda 1549) stronger Lyman alpha and weaker N v lambda 1240, lambda 1400 feature, He II lambda 1640, O III lambda 1663, and Al III lambda 1860. Comparison with other active galactic nuclei (AGN) emission-line regions shows that the ILR spectrum tends to be intermediate between that of the VBLR and that of gas more distant from the ionizing continuum, such as the NLR and extended Lyman alpha nebulosity. Comparison of the spectra with results from photoionization models suggests that, compared with the VBLR, the ILR is about 10 times more distant from the ionizing continuum (approximately 1 pc), 100 to 1000 times less dense (10(exp 10)/cu cm), and has a smaller covering factor (less than or approximately 3%, compared with approximately 24% for the VBLR).

Brotherton, M. S.↗
NASA NTRS1982

Principal Components as a Data Reduction and Noise Reduction Technique

The potential of principal components as a pipeline data reduction technique for thematic mapper data was assessed and principal components analysis and its transformation as a noise reduction technique was examined. Two primary factors were considered: (1) how might data reduction and noise reduction using the principal components transformation affect the extraction of accurate spectral classifications; and (2) what are the real savings in terms of computer processing and storage costs of using reduced data over the full 7-band TM complement. An area in central Pennsylvania was chosen for a study area. The image data for the project were collected using the Earth Resources Laboratory's thematic mapper simulator (TMS) instrument.

Imhoff, M. L.↗
NASA NTRS1985

Preliminary Comparisons of the Information Content and Utility of TM Versus MSS Data

Some preliminary indications were provided as to the relative merits of actual TM data versus MSS data for land cover mapping related applications. Three analyses were designed which had sensitivity to the differences in spectral, spatial and radiometric parameters between the TM and MSS. In the water body analysis, a primarily spatially related test, the detectability of small uniform targets was examined. The principal components analysis, an examination of the inherent dimensionality of the data, was more spectrally and radiometrically related. The spectral clustering analysis, also heavily spectrally and radiometrically influenced, provided information on the types of targets separable on TM versus MSS data. These analyses were to be conducted with simultaneously collected LANDSAT-4 complete TM (7 band) and MSS (4 band) data. In actuality, 4-band TM data, and archived LANDSAT-2 MSS data of the same area were used.

Markham, B. L.↗
NASA NTRS1990

Pattern recognition of clouds and ice in polar regions

The study is based on AVHRR imagery and results from Landsat high-spatial-resolution scenes. Among the textual features investigated are the gray level difference vector (GLDV), and sum and difference histogram (SADH) approaches as well as gray level run length, spatial-coherence, and spectral-histogram measures. The traditional stepwise discriminant analysis and neural-network analysis are used for the identification of 20 Arctic surface and cloud classes. A principal-component analysis and hybrid architecture employing a modularized competitive learning layer are utilized. It is pointed out that the cloud-classification accuracy comparable to that of back-propagation could be achieved with a training time two orders of magnitude faster.

Welch, R. M.↗
NASA NTRS1991

Humidity profiles over the ocean

The variabilities of atmospheric humidity profile over oceans from daily to interannual time scales were examined using 9 years of daily and semidaily radiosonde soundings at island stations extending from the Arctic to the South Pacific. The relative humidity profiles were found to have considerable temporal and geographic variabilities, contrary to the prevalent assumption. Principal component analysis on the profiles of specific humidity were used to examine the applicability of a relation between the surface-level humidity and the integrated water vapor; this relation has been used to estimate large-scale evaporation from satellite data. The first principal component was found to correlate almost perfectly with the integrated water vapor. The fractional variance represented by this mode increases with increasing period. It reaches approximately 90 percent at two weeks and decreases sharply, below one week, down to approximately 60 percent at the daily period. At low frequencies, the integrated water vapor appeared to be an adequate estimator of the humidity profile and the surface-level humidity. At periods shorter than a week, more than one independent estimator is needed.

Liu, W. T.↗
NASA NTRS1990

Data analysis techniques

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

Park, Steve↗