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Reflectance calculations of soybeans and corn crops at two times during the growing season indicate that the high sensitivity of the thematic mapper mid-infrared band to exposed bare soil between soybean rows is most likely responsible for early season spectral discrimination of corn and soybean crops by this band.
The robustness of discriminant functions to nonnormality is investigated. The performance of procedures relative to measures of the difference between the actual distribution of the observations and the usual assumption of normal densities is assessed. For example, the two population, mixed distributions problem with equal costs of misclassification are considered. The parameters are estimated by maximum likelihood and recently proposed robust methods.
The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.
An initial analysis of LANDSAT 4 Thematic Mapper (TM) data for the discrimination of agricultural, forested wetland, and urban land covers is conducted using a scene of data collected over Arkansas and Tennessee. A classification of agricultural lands derived from multitemporal LANDSAT Multispectral Scanner (MSS) data is compared with a classification of TM data for the same area. Results from this comparative analysis show that the multitemporal MSS classification produced an overall accuracy of 80.91% while the TM classification yields an overall classification accuracy of 97.06% correct.
Entropy in information theory is defined as the expected or mean value of the measure of the amount of self-information contained in the ith point of a distribution series x sub i, based on its probability of occurrence p(x sub i). If p(x sub i) is the probability of the ith state of the system in probability space, then the entropy, E(X) = - sigma p(x sub i) logp (x sub i), is a measure of the disorder in the system. Based on this concept, a method was devised which sought to minimize the entropy in a time series in order to construct the signature of the most coherent motions. The constrained minimization was performed using a Lagrange multiplier approach which resulted in the solution of a simultaneous set of non-linear coupled equations to obtain the coherent time series. The application of the method to space-time data taken by a rake of sensors in the near-wall region of a turbulent boundary layer was presented. The results yielded coherent velocity motions made up of locally decelerated or accelerated fluid having a streamwise scale of approximately 100 nu/u(tau), which is in qualitative agreement with the results from other less objective discrimination methods.
A straightforward statistical analysis is presented of the problem of discriminating between two distributed targets in a radar image. The probability of making an error is calculated as a function of the target contrast, the number of samples averaged to form each observation, and the system noise. In the absence of system noise, it is observed that a target contrast of 1 dB could not be detected with less than 10 samples averaged; it is also seen that the error probability is sensitive to the number of samples averaged, as expected. Setting the target contrast at the radiometric resolution, a comparison is made of different definitions for this resolution. The comparison shows that each definition yields a different error probability, ranging from less than 0.05 to 0.35. Attention is also given to the effect of system noise, and it is shown that thermal noise does not have a significant effect on the error probability for a fixed target contrast.
The possibility of rejecting the internal beta-decay background in coaxial germanium detectors by distinguishing between the multi-site energy losses characteristic of photons and the single-site energy losses of electrons in the range 0.2 - 2 MeV is examined. The photon transport was modeled with a Monte Carlo routine. Background rejection by both multiple segmentation and pulse shape discrimination techniques is investigated. The efficiency of a six 1 cm-thick segment coaxial detector operating in coincidence mode alone is compared to that of a two-segment (1 cm and 5 cm) detector employing both front-rear coincidence and PSD in the rear segment to isolate photon events. Both techniques can provide at least 95 percent rejection of single-site events while accepting at least 80 percent of the multi-site events above 500 keV.
Rain depolarization is quantified through the cross-polarization discrimination (XPD) versus attenuation relationship. Such a relationship is derived by curve fitting to a rigorous theoretical model (the multiple scattering model) to determine the variation of the parameters involved. This simple isolation model (SIM) is compared to data from several earth-space link experiments and to three other models.
The use of spectral attributes criteria was investigated, based on measures of statistical distance of separability between thematic classes in MSS digital LANDSAT imagery, in order to select the best subsets of channels in composite colors for the detection and discrimination of lithological units in the lower valley of Curaca River, State of Bahia, Brazil. Three situations were investigated: (1) selection of the three best channels, considering all of the original bands (channels 4, 5, 6, and 7); (2) selection of the three best bands, considering the six MSS band-ratios (channels 4/5, 4/6. 4/7, 5/6, 5/7, and 6/7); and (3) selection of the three best bands in a hybrid approach (the four original bands and the six ratios). A visual analysis was done on color composite images using the selected sets. Results show that the hybrid product (bands 4, 5/7, and 7 with green, blue, and red respectively) and the Normal Color Composite (bands 4, 5, and 7 with blue, green, and red colors respectively) had the best performance.
In this paper, factors controlling the radiance incident on a remote-sensing device and the interaction of the sensor with upwelling radiance are considered. Attention is also given to systematic and random radiance variations across an image: while the former may be corrected for (to an extent) the latter may not, so that it is necessary to be aware of the impact of random variations on target identification, discrimination and quantification. Both the optical reflective and the thermal-infrared parts of the spectrum are considered. Equations are presented to concisely illustrate the processes involved.
Reflectance calculations of soybeans and corn crops at two times during the growing season indicate that the high sensitivity of the thematic mapper mid-infrared band to exposed bare soil between soybean rows is most likely responsible for early season spectral discrimination of corn and soybean crops by this band.
The potential of remote sensing systems to provide a cost-effective inventory tool in the case of forest resources is currently of interest to a variety of natural resources management agencies. A number of studies have been performed regarding the use of Landsat data for mapping forest resources in Michigan. The present paper is concerned with current research, which has been directed toward the development and evaluation of computer-implemented classifications for the identification and characterization of coniferous forest types in Michigan's northern Lower Peninsula. Attention is given to the characteristic response curves from Landsat MSS data, spectral response curve models, and forest cover-type discrimination. It is found that spectral response curve models can be used to evaluate and explain the characteristic spectral responses of coniferous forest types on a snow-covered, winter Landsat scene.
Models for the detection and the discrimination of low-contrast signals by human observers typically assume that the observer is limited by the filtering action of the visual system and by the noisy character of its processing. For some models both the filtering and the noise can be represented by a noise in the stimulus domain, the input equivalent noise of the model. The present study derives some formulas for computing this noise, describes the calculation of a sample, and discusses some implications of this approach.
Multipolarized airborne Synthetic Aperture Radar (SAR) data were acquired over a largely agricultural test site near Macomb, Illinois, in conjunction with the Shuttle Imaging Radar (SIR-B) experiment in October 1984. The NASA/JPL L-band SAR operating at 1.225 GHz made a series of daily overflights with azimuth view angles both parallel and orthogonal to those of SIR-B. The SAR data was digitally recorded in the quadpolarization configuration. An extensive set of ground measurements were obtained throughout the test site and include biophysical and soil measurements of approximately 400 agricultural fields. Preliminary evaluation of some of the airborne SAR imagery indicates a great potential for crop discrimination and assessment of canopy condition. False color composites constructed from the combination of three linear polarizations (HH, VV, and HV) were found to be clearly superior to any single polarization for purposes of crop classification. In addition, an image constructed using the HH return to modulate intensity and the phase difference between HH and VV returns to modulate chroma indicates a clear capability for assessment of canopy height and/or biomass. In particular, corn fields heavily damaged by infestations of corn borer are readily distinguished from noninfested fields.
NASA has developed an airborne SAR that simultaneously yields image data in four linear polarizations in L-band with 10-m resolution over a swath of about 10 km. Signal data are recorded both optically and digitally and annotated in each of the channels to facilitate completely automated digital correlation. Comparison of the relative intensities of the different polarizations furnishes discriminatory mapping information. Local intensity variations in like-polarization images result from topographic effects, while strong cross polarization responses denote the effects of vegetation cover and, in some cases, possible scattering from the subsurface. In each of the areas studied, multiple polarization data led to the discrimination and mapping of unique surface unit features.
This article describes a separability measure for class discrimination. This measure is based on the Fisher information measure for estimating the mixing proportion of two classes. The Fisher information measure not only provides a means to assess quantitatively the information content in the features for separating classes, but also gives the lower bound for the variance of any unbiased estimate of the mixing proportion based on observations of the features. Unlike most commonly used separability measures, this measure is not dependent on the form of the probability distribution of the features and does not imply a specific estimation procedure. This is important because the probability distribution function that describes the data for a given class does not have simple analytic forms, such as a Gaussian. Results of applying this measure to compare the information content provided by three Landsat-derived feature vectors for the purpose of separating small grains from other crops are presented.
The application of TM data to rock discrimination is discussed. Sixteen specific terrains derived from geologic maps are examined on TM images of the Arabian shield obtained on Apr. 14, 1984; visual enhancement procedures are applied to the images. The rock types observed in the test site are described; the major sedimentary formations in the test area are laterite and sandstone. The data reveal that the layered rocks in the outcrop consist of a variety of metamorphosed volcanics, metamorphosed sediments, and amphibolite, and the intrusive complex is composed of several classes of mafic and acidic rocks.
Longitudinal mode selection by injection has been demonstrated as a viable technique for TEA-CO2 lasers with pulse energies of a Joule or greater. Once reliable generation of single-longitudinal-mode (SLM) pulses is obtained, the characteristics and the causes of intrapulse frequency variation can be studied. These include the effect of the decaying plasma, the thermal gradient due to the energy dissipation associated with the laser mechanism itself, and the pressure shift of the center frequency of the laser transition. The use of the positive-branch unstable resonator as an efficient means of coupling a discharge with large spatial dimensions to an optical cavity mode introduces another concern: namely, what can be done to emphasize transverse mode discrimination in an unstable resonator cavity while maintaining high coupling efficiency. These issues are discussed in this paper, and relevant experimental results are included.